Systems and methods for analyzing power quality events in an electrical system

ABSTRACT

A method for analyzing power quality events in an electrical system includes processing electrical measurement data from or derived from energy-related signals captured by at least one of a plurality of metering devices in the electrical system to generate or update a plurality of dynamic tolerance curves. Each of the plurality of dynamic tolerance curves characterizes a response characteristic of the electrical system at a respective metering point of a plurality of metering points in the electrical system. Power quality data from the plurality of dynamic tolerance curves is selectively aggregated to analyze power quality events in the electrical system.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/252,112, filed on Jan. 18, 2019, which claims the benefit of andpriority to U.S. Provisional Application No. 62/694,791, filed on Jul.6, 2018; U.S. Provisional Application No. 62/770,730, filed on Nov. 21,2018; U.S. Provisional Application No. 62/770,732, filed on Nov. 21,2018; U.S. Provisional Application No. 62/770,737, filed on Nov. 21,2018; U.S. Provisional Application No. 62/770,741, filed on Nov. 21,2018; and U.S. Provisional Application No. 62/785,424, filed on Dec. 27,2018, which applications were filed under 35 U.S.C. § 119(e). Theforegoing applications are incorporated by reference herein in theirentirety.

FIELD

This disclosure relates generally to power quality issues, and moreparticularly, to systems and methods for analyzing power quality issuesor events in an electrical system.

BACKGROUND

As is known, power quality issues are one of the most significant andcostly impacts on electrical systems (also sometimes referred to as“electrical networks”). Poor power quality is estimated to cost theEuropean economy up to €150 billion annually, according to the LeonardoPower Quality Initiative.¹ Additionally, the U.S. economy experienceslosses ranging from $119 billion to $188 billion annually, according toresearch by the Electric Power Research Institute (EPRI).² Perhaps themost important statistic is the EPRI finding that 80 percent ofpower-quality disturbances are generated within a facility. Oneexemplary economic model summarizes the total cost associated with powerquality events as follows:¹https://adpowertuning.com/en/about-us/news-stories/148-leonardo-energy-qpan-european-power-quality-surveyq-shows-g150bn-annually-in-cost-for-low-power-quality.html²https://blog.schneider-electric.com/power-management-metering-monitoring-power-quality/2015/10/16/why-poor-power-quality-costs-billions-annually-and-what-can-be-done-about-it/

-   -   Total losses=production losses+restart losses+product/material        losses+equipment losses+third-party costs+other miscellaneous        costs³ ³ The Cost of Poor Power Quality, Roman Targosz and David        Chapman, October 2015, ECI Publication No. Cu0145

Other miscellaneous costs associated with power quality issues mayinclude intangible losses such as a damaged reputation with customersand suppliers or more direct losses such as the devaluation of creditratings and stock prices.

SUMMARY

Described herein are systems and methods related to analyzing powerquality issues or events in an electrical system. The electrical systemmay be associated with at least one load, process, building, facility,watercraft, aircraft, or other type of structure, for example. In oneaspect of this disclosure, a method for analyzing power quality eventsin an electrical system includes processing electrical measurement datafrom or derived from energy-related signals captured by a plurality ofmetering devices (e.g., intelligent electronic devices (IEDs)) in theelectrical system to generate or update a plurality of dynamic tolerancecurves. In some embodiments, each of the plurality of dynamic tolerancecurves characterizes a response characteristic of the electrical systemat a respective metering point of a plurality of metering points in theelectrical system. The method also includes selectively aggregatingpower quality data from the plurality of dynamic tolerance curves toanalyze power quality events in the electrical system.

In some embodiments, the method may be implemented using one or more ofthe plurality of metering devices. Additionally, in some embodiments themethod may be implemented remote from the plurality of metering devices,for example, in a gateway, a cloud-based system, on-site software, aremote server, etc. (which may alternatively be referred to as a“head-end” system herein). In some embodiments, the plurality ofmetering devices may be coupled to measure electrical signals, receiveelectrical measurement data from the electrical signals at an input, andconfigured to generate at least one or more outputs. The outputs may beused to analyze power quality events in an electrical system. Examplesof the plurality of metering devices may include a smart utility meter,a power quality meter, and/or another metering device (or devices). Theplurality of metering devices may include breakers, relays, powerquality correction devices, uninterruptible power supplies (UPSs),filters, and/or variable speed drives (VSDs), for example. Additionally,the plurality of metering devices may include at least one virtual meterin some embodiments.

In embodiments, the above method is generally applicable to non-periodicpower quality issues or events such as transients, short-duration rmsvariations (e.g., sags, swells, momentary interruptions, temporaryinterruptions, etc.), and some long-duration rms variations (e.g., thatmay be as long as about 1-5 minute(s)).

Examples of electrical measurement data that may be captured by theplurality of metering devices may include at least one of continuouslymeasured voltage and current signals and their derived parameters andcharacteristics. Electrical parameters and events may be derived, forexample, from analyzing energy-related signals (e.g., real power,reactive power, apparent power, harmonic distortion, phase imbalance,frequency, voltage/current transients, voltage sags, voltage swells,etc.). More particularly, the plurality of metering devices may evaluatea power quality event's magnitude, duration, load impact, recovery timefrom impact, unproductive recovery energy consumed, CO2 emissions fromrecovery energy, costs associated with the event, and so forth.

It is understood there are types of power quality events and there arecertain characteristics of these types of power quality events, asdescribed further below in connection with paragraph [0029] and thetable from IEEE Standard 1159-2009 (known art) provided beneathparagraph [0029], for example. A voltage sag is one example type ofpower quality event. The distinctive characteristics of voltage sagevents are the magnitude of the voltage sag and its duration, forexample. As used herein, examples of power quality events may includevoltage events impacting phase, neutral, and/or ground conductors and/orpaths.

The above method, and the other methods (and systems) described below,may include one or more of the following features either individually orin combination with other features in some embodiments. In someembodiments, the energy-related signals captured by the plurality ofmetering devices include at least one of: voltage, current, energy,active power, apparent power, reactive power, harmonic voltages,harmonic currents, total voltage harmonic distortion, total currentharmonic distortion, harmonic power, individual phase currents,three-phase currents, phase voltages, and line voltages. In embodiments,the energy-related signals may include (or leverage) substantially anyelectrical parameter derived from voltage and current signals (includingthe voltages and currents themselves), for example.

In some embodiments, processing electrical measurement data from orderived from energy-related signals captured by a plurality of meteringdevices in the electrical system to generate or update a plurality ofdynamic tolerance curves, includes: processing electrical measurementdata from or derived from energy-related signals captured by theplurality of metering devices at a first, initial time to generate adynamic tolerance curve for each respective metering point of aplurality of metering points in the electrical system based on aresponse characteristic of the electrical system at each respectivemetering point at the first, initial time. Electrical measurement datafrom or derived from energy-related signals captured by the plurality ofmetering devices at subsequent times after the first, initial time maybe continued to be processed to optimize the dynamic tolerance curve foreach respective metering point based on a response characteristic of theelectrical system at each respective metering point at the subsequenttimes.

In some embodiments, degradations or improvements in the electricalsystem's sensitivity or resilience to power quality events at eachrespective metering point may be identified and reported. In one aspect,the degradations or improvements are identified based identified changesin the dynamic tolerance curve for each respective metering point.Additionally, in one aspect the identified degradations or improvementsare reported by: generating and/or initiating a warning indicating theidentified degradations or improvements in the electrical system'ssensitivity or resilience to power quality events, and communicating thewarning via at least one of: a report, a text, an email, audibly, and aninterface of a screen/display. In some embodiments, the warning providesactionable recommendations for responding to the identified degradationsor improvements in the electrical system's sensitivity or resilience topower quality events.

In some embodiments, selectively aggregating power quality data from theplurality of dynamic tolerance curves to analyze power quality events,includes: selectively aggregating power quality data from the pluralityof dynamic tolerance curves to generate at least one aggregate dynamictolerance curve, and analyzing the at least one aggregated dynamictolerance curve to consider power quality events in the electricalsystem. In one aspect, the power quality data is selectively aggregatedbased on locations of the plurality of metering points in the electricalsystem. Additionally, in one aspect the power quality data isselectively aggregated based on criticality or sensitivity of theplurality of metering points to power quality events.

In some embodiments, selectively aggregating power quality data from theplurality of dynamic tolerance curves to analyze power quality events,includes: determining if any discrepancies exist between the selectivelyaggregated power quality data, and requesting additional informationfrom a system user to reconcile the discrepancies. The discrepancies mayinclude, for example, inconsistent naming conventions in the selectivelyaggregated data.

In some embodiments, selectively aggregating power quality data from theat least one dynamic tolerance curve to consider power quality events,includes: determining a relative criticality score of each of the powerquality events to a process or an application associated with theelectrical system. In one aspect, the relative criticality score isbased on an impact of the power quality events to the process or theapplication. In one aspect, the impact of the power quality events isrelated to tangible or intangible costs associated with the powerquality events to the process or the application. Additionally, in oneaspect the impact of the power quality events is related to relativeimpact on loads in the electrical system. In some embodiments, thedetermined relative criticality score may be used to prioritizeresponding to the power quality events.

In some embodiments, power quality events in each of the plurality ofdynamic tolerance curves may be tagged with relevant and characterizinginformation based on information extracted about the power qualityevents. Examples of relevant and characterizing information may includeat least one of: severity (magnitude), duration, power quality type(e.g., sag, swell, interruption, oscillatory transient, impulsivetransient, etc.), time of occurrence, process(es) involved, location,devices impacted, relative or absolute impact, recovery time,periodicity of events or event types, etc. Additionally, examples ofrelevant and characterizing information may include at least one of:information about activities occurring before, during, or after thepower quality events, such as a measured load change correlating with anevent, or a particular load or apparatus switching on or off before,during or after the event. In some embodiments, the information aboutthe power quality events may be extracted from portions of theelectrical measurement data taken prior to a start time of the powerquality events, and from portions of the electrical measurement datataken after a conclusion of the power quality events. Additionally, insome embodiments the information about the power quality events may beextracted from portions of the electrical measurement data taken duringthe power quality events.

In some embodiments, processing electrical measurement data from orderived from energy-related signals captured by a plurality of meteringdevices in the electrical system to generate or update a plurality ofdynamic tolerance curves, includes: receiving electrical measurementdata from or derived from energy-related signals captured by theplurality of metering devices in the electrical system during afirst-time period correspond to a learning period. One or more upperalarm thresholds and one or more lower alarm thresholds may be generatedfor each of the plurality of dynamic tolerance curves based on theelectrical measurement data captured during the first-time period.Electrical measurement data from or derived from energy-related signalscaptured by the plurality of metering devices during a second-timeperiod corresponding to a normal-operation period may be received, andit may be determined if at least one of the one or more upper alarmthresholds and the one or more lower alarm thresholds needs to beupdated based on the electrical measurement data received during thesecond-time period. In response to determining that at least one of theone or more upper alarm thresholds and the one or more lower alarmthresholds needs to be updated, the at least one of the one or moreupper alarm thresholds and the one or more lower alarm thresholds may beupdated.

In some embodiments, the one or more upper alarm thresholds include athreshold above a nominal or expected voltage at the point ofinstallation of the respective one of the plurality of metering devicesin the electrical system. In one aspect, the threshold above the nominalvoltage is indicative of transients, swells, or overvoltages. In someembodiments, the one or more lower alarm thresholds include a thresholdbelow a nominal or expected voltage at the point of installation of therespective one of the plurality of metering devices in the electricalsystem. In one aspect, the threshold below the nominal voltage isindicative of sags, interruptions, notches, or undervoltages. In someembodiments, each of the one or more upper alarm thresholds and each ofthe one or more lower alarm thresholds has an associated magnitude andduration.

In some embodiments, each of the plurality of metering devices in theelectrical system is associated with a respective one of the pluralityof metering points. Additionally, in some embodiments at least one ofthe plurality of metering devices includes a virtual meter. In someembodiments, the plurality of dynamic tolerance curves may beselectively displayed in at least one of: a graphical user interface(GUI) of a control system used for controlling one or more parametersassociated with the electrical system, and a GUI of at least one of theplurality of metering devices.

In some embodiments, dynamic tolerance curves according to thedisclosure can warn a user of any (or substantially any) degradation orimprovement of the electrical system's sensitivity or resilience (e.g.,if the impact is defined by the time to get back to normal). This can bedone either on a new calculated optimal model, or on a more traditionalkey performance indicator (KPI), such as the evolution of the percentageof loads dropped off.

Typically an electrical system's sensitivity or resilience does not stayat exactly the same level (or levels) over time. Degradations may occur,but also improvements due, for example, to normal life operations,changes in equipment, maintenance operations, time of impact, etc. Awarning indicating a degradation or improvement can be provided withinan existing live system (such as a Power or Industrial/manufacturingSCADA system, a building management system, a Power Monitoring system).As another example, a specific report may be issued and sent via emailor provided online on a mobile application.

In these cases, specific meter related actionable recommendations may besent to a user or a partner or a services team to provide them withoptional or compulsory actions to perform.

In another aspect of this disclosure, a system for analyzing powerquality events in an electrical system includes at least one inputcoupled to at least a plurality of metering devices in the electricalsystem, and at least one output coupled to at least a plurality of loadsmonitored by the plurality of metering devices. The system for managingpower quality events also includes a processor coupled to receiveelectrical measurement data from or derived from energy-related signalscaptured by the plurality of metering devices from the at least onesystem input. The processor may be configured to process the electricalmeasurement data to generate or update at least one of a plurality ofdynamic tolerance curves. In some embodiments, at least one of aplurality of dynamic tolerance curves characterizes and/or depicts aresponse characteristic of the electrical system at an at leastrespective metering point of a plurality of metering points in theelectrical system. The processor may also be configured to selectivelyaggregate power quality data from the plurality of dynamic tolerancecurves, and analyze power quality events in the electrical system basedon the selectively aggregated power quality data. The processor may befurther configured to adjust at least one parameter associated with oneor more of the plurality of loads in response to the power qualityevents analyzed. In some embodiments, the at least one parameter isadjusted in response to a control signal generated at the at least onesystem output and provided to the one or more of the plurality of loads.

In some embodiments, the energy-related signals captured by theplurality of metering devices include at least one of: voltage, current,energy, active power, apparent power, reactive power, harmonic voltages,harmonic currents, total voltage harmonic distortion, total currentharmonic distortion, harmonic power, individual phase currents,three-phase currents, phase voltages, and line voltages.

In some embodiments, the metering devices (e.g., IEDs) and loads of theabove and below described systems and methods are installed, located orderived from different respective locations (i.e., a plurality oflocations) or metering points in the electrical system. A particular IED(e.g., a second IED) may be upstream from another IED (e.g., a thirdIED) in the electrical system while being downstream from a further IED(e.g., a first IED) in the electrical system, for example.

As used herein, the terms “upstream” and “downstream” are used to referto electrical locations within an electrical system. More particularly,the electrical locations “upstream” and “downstream” are relative to anelectrical location of an IED collecting data and providing thisinformation. For example, in an electrical system including a pluralityof IEDs, one or more IEDs may be positioned (or installed) at anelectrical location that is upstream relative to one or more other IEDsin the electrical system, and the one or more IEDs may be positioned (orinstalled) at an electrical location that is downstream relative to oneor more further IEDs in the electrical system. A first IED or load thatis positioned on an electrical circuit upstream from a second IED orload may, for example, be positioned electrically closer to an input orsource of the electrical system (e.g., a utility feed) than the secondIED or load. Conversely, a first IED or load that is positioned on anelectrical circuit downstream from a second IED or load may bepositioned electrically closer to an end or terminus of the electricalsystem than the other IED.

A first IED or load that is electrically connected in parallel (e.g., onan electrical circuit) with a second IED or load may be considered to be“electrically” upstream from said second IED or load in embodiments, andvice versa. In embodiments, algorithm(s) used for determining adirection of a power quality event (i.e., upstream or downstream) is/arelocated (or stored) in the IED, cloud, on-site software, gateway, etc.As one example, the IED can record an electrical event's voltage andcurrent phase information (e.g., by sampling the respective signals) andcommunicatively transmit this information to a cloud-based system. Thecloud-based system may then analyze the voltage and current phaseinformation (e.g., instantaneous, root-mean-square (rms), waveformsand/or other electrical characteristic) to determine if the source ofthe voltage event was electrically upstream or downstream from where theIED is electrically coupled to the electrical system (or network).

As used herein, a load loss (sometimes also referred to as a “loss ofload”) is the unexpected, unplanned and/or unintentional removal of oneor more loads from the electrical system. In this application, a voltageperturbation or event, and the subsequent load loss, is likely a resultof one or more external influences on the electrical system (e.g., afault, etc.), or the normal or abnormal operation of loads, protectivedevices, mitigation devices, and/or other equipment intentionallyconnected to the electrical system. Load losses may be indicated bymeasured parameters such as voltage, current, power, energy, harmonicdistortion, imbalance, etc., or they may be indicated by discrete(digital) and/or analog input-output (I/O) signals originating fromequipment directly and/or indirectly connected to the electrical system.For example, breakers often provide an output indication on theirpresent position (e.g., open/closed, off/on, etc.) to communicate theiroperational status.

In some embodiments, the electrical measurement data from energy-relatedsignals captured by the plurality of metering devices may be processedon one or more or the plurality of metering devices, or be processed inon-site software, in a cloud-based application, or in a gateway, etc.,to characterize power quality events in the electrical system.Additionally, in some embodiments the electrical measurement data may beprocessed on a system for quantifying power quality events in anelectrical system, for example, a control system associated with theelectrical system. The control system may be used for controlling one ormore parameters associated with the electrical system, for example. Inembodiments, identifying the power quality event may includeidentifying: (a) a type of power quality event, (b) a magnitude of theanomalous power quality event, (c) a duration of the power qualityevent, and/or (d) a location of the power quality event in theelectrical system. In embodiments, the power quality event type mayinclude one of a voltage sag, a voltage swell, a voltage interruption,and a voltage transient. Additionally, in embodiments the location ofthe power quality event may be derived from voltage and current signalsas measured by the IEDs and associated with the anomalous voltagecondition.

As discussed above, a voltage event is one example type of power qualityevent. A power quality event may include at least one of a voltage sag,a voltage swell, and a voltage transient, for example. According to IEEEStandard 1159-2009, for example, a voltage sag is a decrease to between0.1 and 0.9 per unit (pu) in rms voltage or current at the powerfrequency for durations of 0.5 cycle to 1 min. Typical values are 0.1 to0.9 pu. Additionally, according to IEEE Standard 1159-2009, a voltageswell is an increase in rms voltage or current at the power frequencyfor durations from 0.5 cycles to 1 min. Below is a table from IEEEStandard 1159-2009 (known art), which defines various categories andcharacteristics of power system electromagnetic phenomena.

Typical Typical

Typical voltage Categories content duration magnitude 1.0

1.1 Impulsive 1.1.1 Nanosecond 5 μs rise <50 ns 1.1.2 Microsecond 1 μsrise 50 ns-1ms 1.1.3 Millisecond 0.1 ms rise >1 ms 1.2 Oscillatory 1.2.1Low frequency <5 kHz 0.3-50 ms 0-4 pu 1.2.2 Medium frequency 5-500 kHz20 μs 0-8 pu 1.2.3 High frequency 0.5-5 kHz

 μs 0-4 pu 2.0 Short duration variations 2.1

2.1.1 Sag 0.5-30 cycles 0.1-0.9 pu 2.1.2 Swell 0.5-30 cycles 1.1-1.8 pu2.2 Momentary 2.2.1 Interruption 0.5 cycles-3 s <0.1 pu 2.2.2 Sag  30cycles-3 s 0.3-0.9 pu 2.2.3 Swell  30 cycles-3 s 1.1-1.4 pu 2.3Temporary 2.3.1 Interruption 3 s-1 min <0.1 pu 2.3.2 Sag 3 s-1 min0.1-0.9 pu 2.3.3 Swell 3 s-1 min 1.1-1.2 pu 3.0 Long duration variations3.1 Interruption,

>1 min 0.0 pu 3.2 Undervoltage >1 min 0.8-0.9 pu 3.3 Overvoltage >1 min1.1-1.2 pu 4.0 Voltage* steady state 0.5-2% 5.0 Waveforms distortion 5.1DC offset steady state  0-0.1% 5.2 Harmonies 0-1000

steady state  0-20% 5.3 Interharmonies 0-6 kHz steady state   0-2% 5.4Notching steady state 5.5 Noise broad-band steady state   0-1% 6.0Voltage

<25 Hz intermittent 0.1-7% 7.0 Power frequency

<10

indicates data missing or illegible when filed

It is understood that the above table is one standards body's (IEEE inthis case) way of defining/characterizing power quality events. It isunderstood there are other standards that define power qualitycategories/events as well, such as the International ElectrotechnicalCommission (IEC), American National Standards Institute (ANSI), etc.,which may have different descriptions or power quality event types,characteristics, and terminology. In embodiments, power quality eventsmay be customized power quality events (e.g., defined by a user).

In some embodiments, the electrical measurement data processed toidentify the power quality event may be continuously orsemi-continuously captured by the plurality of metering devices, and thetolerance curves may be dynamically updated in response to power qualityevents detected (or identified) from the electrical measurement data.For example, the tolerance curve may initially be generated in responseto power quality events identified from electrical measurement datacaptured at a first time, and may be updated or revised in response to(e.g., to include or incorporate) power quality events identified fromelectrical measurement data captured at a second time. As events arecaptured, the tolerance curve (also sometimes referred to herein as “adynamic tolerance curve”) may be continuously (e.g., dynamically)updated according to the unique response of the electrical system.

In some embodiments, the tolerance curve may be displayed in a GUI of atleast one IED, or a GUI of a control system used for monitoring orcontrolling one or more parameters associated with the electricalsystem. In embodiments, the control system may be a meter, an IED,on-site/head-end software (i.e., a software system), a cloud-basedcontrol system, a gateway, a system in which data is routed over theEthernet or some other communications system, etc. A warning may bedisplayed in the GUI of the IED, the monitoring system or the controlsystem, for example, in response to a determined impact (or severity) ofthe power quality event being outside of a range or threshold. In someembodiments, the range is a predetermined range, for example, a userconfigured range. Additionally, in some embodiments the range isautomatic, for example, using standards-based thresholds. Further, insome embodiments the range is “learned,” for example, by starting with anominal voltage and pushing out the thresholds as non-impactful eventsoccur in the natural course of the electrical network's operation.

The GUI may be configured to display factors contributing to the powerquality event. Additionally, the GUI may be configured to indicate alocation of the power quality event in the electrical system. Further,the GUI may be configured to indicate how loads (or another specificsystem or piece of equipment in the electrical system) will respond tothe power quality event. It is understood that any number of informationmay be displayed in the GUI. As part of this invention, any electricalparameter, impact to a parameter, I/O status input, I/O output, processimpact, recovery time, time of impact, phases impacted, potentiallydiscrete loads impacted beneath a single IED, etc. may be displayed inthe GUI. FIG. 20, for example, as will be discussed further below, showsa simple example of incorporating percent load impacted with anindication of recovery time.

In embodiments, the tolerance curve displayed in the GUI does not havefixed scaling but, rather, can (and needs to) auto-scale, for example,to capture or display a plurality of power quality events. In accordancewith various aspects of the disclosure, the beauty of having a dynamictolerance curve is not being constrained to a static curve or curves(e.g., with fixed scaling). For example, referring briefly to FIG. 2(which will be discussed further below), while the y-axis is shown as apercent of nominal in FIG. 2, it can also be shown as an absolutenominal value (e.g., 120 volts, 208 volts, 240 volts, 277 volts, 480volts, 2400 volts, 4160 volts, 7.2 kV, 12.47 kV, etc.). In this case,auto-scaling would be required because different voltage ranges wouldrequire different scaling for the y-axis. Additionally, the x-axis maybe scaled in different units (e.g., cycles, seconds, etc.) and/or mayhave a variable maximum terminus point (e.g., 10 seconds, 1 minute, 5minutes, 600 cycles, 3600 cycles, 18,000 cycles, etc.). In other words,in some embodiments there is no reason for the GUI to show more than ithas to.

In embodiments, a goal of the invention claimed herein is to build acustomized tolerance curve for a discrete location within a customer'spower system (e.g., at a given IED) based on a perceived impact todownstream loads. Additionally, in embodiments a goal of the inventionclaimed herein is to quantify the time it takes to recover from a powerquality event. In short, aspects of the invention claimed herein aredirected toward describing the impact of a power quality event, whichallows a customer to understand their operational parameters andconstraints, accordingly.

As used herein, an IED is a computational electronic device optimized toperform a particular function or set of functions. As discussed above,examples of IEDs include smart utility meters, power quality meters, andother metering devices. IEDs may also be imbedded in variable speeddrives (VSDs), uninterruptible power supplies (UPSs), circuit breakers,relays, transformers, or any other electrical apparatus. IEDs may beused to perform monitoring and control functions in a wide variety ofinstallations. The installations may include utility systems, industrialfacilities, warehouses, office buildings or other commercial complexes,campus facilities, computing co-location centers, data centers, powerdistribution networks, and the like. For example, where the IED is anelectrical power monitoring device, it may be coupled to (or beinstalled in) an electrical power distribution system and configured tosense and store data as electrical parameters representing operatingcharacteristics (e.g., voltage, current, waveform distortion, power,etc.) of the power distribution system. These parameters andcharacteristics may be analyzed by a user to evaluate potentialperformance, reliability or power quality-related issues. The IED mayinclude at least a controller (which in certain IEDs can be configuredto run one or more applications simultaneously, serially, or both),firmware, a memory, a communications interface, and connectors thatconnect the IED to external systems, devices, and/or components at anyvoltage level, configuration, and/or type (e.g., AC, DC). At leastcertain aspects of the monitoring and control functionality of a IED maybe embodied in a computer program that is accessible by the IED.

In some embodiments, the term “IED” as used herein may refer to ahierarchy of IEDs operating in parallel and/or tandem. For example, aIED may correspond to a hierarchy of energy meters, power meters, and/orother types of resource meters. The hierarchy may comprise a tree-basedhierarchy, such a binary tree, a tree having one or more child nodesdescending from each parent node or nodes, or combinations thereof,wherein each node represents a specific IED. In some instances, thehierarchy of IEDs may share data or hardware resources and may executeshared software.

The features proposed in this disclosure evaluate specific power qualityevents to characterize their impact on loads of an electrical system,recovery time, and other useful or interesting parameters. Its scope mayinclude discrete metered points, network zones, and/or the aggregatedelectrical system in total. Novel ideas to display these concepts arealso discussed, allowing the energy consumer to more efficiently andcost-effectively identify, analyze, mitigate, and manage theirelectrical networks.

Of the seven recognized power quality categories defined by IEEE1159-2009, short-duration root mean square (rms) variations aregenerally the most disruptive and have the largest universal economicimpact on energy consumers. Short-duration rms variations includevoltage sags/dips, swells, instantaneous interruptions, momentaryinterruptions and temporary interruptions. One example study by theElectric Power Research Institute (EPRI) estimates an average of about66 voltage sags are experienced by industrial customers each year. Asthe trend of industries becoming more dependent on sag-sensitiveequipment has increased, so has the impact of these events.

The prevalence of voltage sags and the consequences of a growing installbase of sag-sensitive equipment present many additional opportunitiesfor electric solutions and services providers. The table belowillustrates several example opportunities:

Opportunities Benefits Solutions Increased monitoring systems componentsMore suitable sag-immunity equipment Targeted sag mitigation equipmentServices Engineering and consulting Remote monitoring and diagnosticsEquipment installation

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of the disclosure, as well as the disclosureitself may be more fully understood from the following detaileddescription of the drawings, in which:

FIG. 1 shows a graphical view of several example power qualitycategories;

FIG. 1A shows an example electrical system in accordance withembodiments of the disclosure;

FIG. 1B shows an example intelligent electronic device (IED) that may beused in an electrical system in accordance with embodiments of thedisclosure;

FIG. 2 shows an example Information Technology Industry (ITI) curve(also sometimes referred to as a “power acceptability curve”);

FIG. 3 shows an example baseline voltage tolerance curve which could bethe ITI curve (as illustrated) or some other unique relationship betweenan event's voltage magnitude and duration;

FIG. 4 shows an example voltage sag event on a baseline voltagetolerance curve;

FIG. 5 shows an example recommended change to the baseline voltagetolerance curve of FIG. 3 based on an impact of the voltage sag eventshown in FIG. 4;

FIG. 6 shows an example dynamically customized and updated voltagetolerance curve;

FIG. 7 shows an example of a multitude of impactful and non-impactfulvoltage sags, swells, and transients on a voltage tolerance curve;

FIG. 8 shows a dynamically customized and updated voltage tolerancecurve for a multitude of impactful and non-impactful events;

FIG. 9 shows an example three-dimensional (3-D) tolerance-impact curvewith load(s) impact;

FIG. 10 shows an example 3-D tolerance-impact curve with gradient colorshading indicating severity of load(s) impact;

FIG. 11 shows an example 3-D tolerance-impact curve with a sample eventindicating severity of load(s) impact;

FIG. 12 shows an example 3-D tolerance-impact curve with recovery time;

FIG. 13 shows an example 3-D tolerance-impact curve with gradient colorshading indicating length of recovery time;

FIG. 14 shows an example 3-D tolerance-impact curve with a sample eventindicating length of recovery time;

FIG. 15 shows another example 3-D tolerance-impact curve with a sampleevent indicating production losses as an economic impact;

FIG. 16 shows an example simple electrical network with a fault;

FIG. 16A shows another example electrical network with a fault;

FIG. 17 shows an example customized tolerance curve with a multitude ofimpactful and non-impactful upstream and downstream events;

FIG. 18 shows an example customized tolerance curve with a multitude ofimpactful and non-impactful disaggregated upstream events;

FIG. 19 shows an example customized tolerance curve with a multitude ofimpactful and non-impactful disaggregated downstream events;

FIG. 20 shows an example 3-D tolerance-impact curve with load impact,recovery time and upstream/downstream event sources indicated for amultitude of events;

FIG. 21 is a diagram showing an example progression of costs to mitigatevoltage events;

FIG. 22 shows an example customized and updated tolerance curve for thevoltage sag event illustrated in FIG. 4;

FIG. 23 shows the SEMI F47 curve superimposed on the plot illustrated inFIG. 22;

FIG. 24 shows example ride-through benefits of a sag mitigation devicein an electrical system, one example of which is SagFighter® bySchneider Electric;

FIG. 25 shows an example of a multitude of potentially avoided loadimpact events with a sag mitigation device;

FIG. 26 shows another example of a multitude of potentially avoided loadimpacting events and their aggregated recovery time with a sagmitigation device;

FIG. 27 shows an example of the predicted impact of installing a voltageevent mitigation device;

FIG. 28 shows an example of the actual impact of installing a voltageevent mitigation device;

FIG. 29 shows an example of a simple electrical system with a pluralityof IEDs;

FIG. 30 shows an example recovery timeline for a plurality of IED typesexperiencing a voltage event;

FIG. 30A illustrates an example of virtual metering being used toidentify an impact of a voltage event on unmetered loads;

FIG. 30B shows an example electrical system in accordance withembodiments of this disclosure;

FIGS. 30C-30E show example dynamic tolerance curves in accordance withembodiments of this disclosure;

FIG. 30E-30I show further example electrical systems in accordance withembodiments of this disclosure;

FIG. 31 shows an example fault on the simple electrical system of FIG.29;

FIG. 32 shows example zones of the simple electrical system of FIG. 29,for example, based on step-down transformer locations;

FIG. 33 shows an example customized zone configuration of the simpleelectrical system of FIG. 29;

FIG. 34 shows an example of a simple voltage tolerance curve (alsosometimes referred to as a power acceptability curve);

FIG. 35 shows an example voltage sag event shown on the simple voltagetolerance curve of FIG. 34;

FIG. 36 shows an example updated voltage tolerance curve after thevoltage sag event illustrated in FIG. 35;

FIG. 37 shows an example second voltage sag event on the voltagetolerance curve illustrated in FIG. 36;

FIG. 38 shows an example updated voltage tolerance curve after thesecond voltage sag event illustrated in FIG. 37;

FIG. 39 shows a third example voltage sag event on the voltage tolerancecurve illustrated in FIG. 38;

FIG. 40 shows an example voltage tolerance curve after the third voltagesag event illustrated in FIG. 39;

FIG. 41 is a plot showing measured load(s) versus time for an exampleimpactful voltage event;

FIG. 42 is a plot showing measured load(s) versus time for multipleexample impactful voltage events;

FIG. 43 is a plot showing measured, typical and expected load(s) versustime for an example voltage event;

FIG. 44 is a plot showing percent load impact versus time;

FIG. 45 is a flowchart illustrating an example method for managing powerquality events (or disturbances) in an electrical system;

FIG. 46 is a flowchart illustrating an example method for quantifyingpower quality events (or disturbances) in an electrical system;

FIG. 47 is a flowchart illustrating an example method for expandedqualified lead generation for power quality;

FIG. 48 is a flowchart illustrating an example method for generating adynamic tolerance curve for power quality;

FIG. 49 shows an illustrative waveform;

FIG. 50 shows another illustrative waveform;

FIG. 51 is a flowchart illustrating an example method for characterizingpower quality events in an electrical system;

FIG. 52 is a flowchart illustrating an example method for characterizingan impact of a power quality event on an electric system;

FIG. 53 is a flowchart illustrating an example method for reducingrecovery time from a power quality event in an electrical system, forexample, by tracking a response characteristic of the electrical system.

FIG. 54 is a flowchart illustrating an example method for analyzingpower quality events in an electrical system;

FIG. 55 is a flowchart illustrating an example method for generatingdynamic tolerance curves;

FIG. 55A illustrates an example method for moving from an individualmeters thresholds calculation to a group of meters thresholdscalculation;

FIG. 56 is a flowchart illustrating an example method for taggingcriticality scores, for example, during a learning period;

FIG. 57 is a flowchart illustrating another example method for taggingcriticality scores, for example, during a learning period;

FIGS. 57A-57K illustrate several example ways in which loss of load maybe modeled in accordance with embodiments of this disclosure;

FIG. 58 is a flowchart illustrating an example method for generatingdynamic tolerance curves, for example, after a learning period; and

FIG. 59 is a flowchart illustrating an example method for leveraging andcombining dynamic tolerance curves.

DETAILED DESCRIPTION

The features and other details of the concepts, systems, and techniquessought to be protected herein will now be more particularly described.It will be understood that any specific embodiments described herein areshown by way of illustration and not as limitations of the disclosureand the concepts described herein. Features of the subject matterdescribed herein can be employed in various embodiments withoutdeparting from the scope of the concepts sought to be protected.

For convenience, certain introductory concepts and terms used in thespecification (and adopted from IEEE Standard 1159-2009) are collectedhere. Several of these concepts and terms are shown in FIG. 1, forexample. It is notable that FIG. 1 does not include all power qualitycategories such as waveform distortion, imbalance, voltage fluctuations,and power frequency deviations.

As used herein, the term “aperiodic event” is used to describe anelectrical event that occurs non-cyclically, arbitrarily or withoutspecific temporal regularity. For the sake of this paper, bothshort-duration root-mean-square (rms) variations and transients areconsidered to be aperiodic events (i.e., notching is considered as aharmonic phenomenon).

As used herein, the term “instantaneous interruption” is used todescribe a deviation to 0-10% of the nominal value for a duration of ½cycle to 30 cycles.

As used herein, the term “momentary interruption” is used to describe adeviation to 0-10% of the nominal value for a duration of 30 cycles to 3seconds.

As used herein, the term “sag” (of which a “voltage sag” is one example)is used to describe a deviation to 10-90% of the nominal value, forexample, for a duration of ½ cycle to 1 minute, as shown in FIG. 1.

As used herein, the term “short-duration rms variations” is used todescribe a deviation from the nominal value with a duration of ½ cycleto 1 minute. Sub-categories of short-duration rms variations includeinstantaneous interruptions, momentary interruptions, temporaryinterruptions, sags and swells.

As used herein, the term “swell” is used to describe a deviation greaterthan 110% of the nominal value, for example, for a duration of ½ cycleto 1 minute, as shown in FIG. 1.

As used herein, the term “temporary interruption” is used to describe adeviation to 0-10% of the nominal value for a duration of 3 seconds to 1minute.

As used herein, the term “transient” is used to describe a deviationfrom the nominal value with a duration less than 1 cycle. Sub-categoriesof transients include impulsive (uni-direction polarity) and oscillatory(bi-directional polarity) transients.

In embodiments, the degree of impact a short-duration rms variation hason an energy consumer's facility is primarily dependent on four factors:

1. The nature and source of the event,

2. The susceptibility of the load(s) to the event,

3. The event's influence on the process or activity, and

4. The cost sensitivity to this event.

Consequently, each customer system, operation or load may responddifferently to a given electrical perturbation. For example, it ispossible for a voltage sag event to significantly impact one customer'soperation while the same voltage sag may have little or no noticeableimpact on another customer's operation. It is also possible for avoltage sag to impact one part of a customer's electrical systemdifferently than it does another part of the same electrical system.

Referring to FIG. 1A, an example electrical system in accordance withembodiments of the disclosure includes one or more loads (here, loads111, 112, 113, 114, 115) and one or more intelligent electronic devices(IEDs) (here, IEDs 121, 122, 123, 124) capable of sampling, sensing ormonitoring one or more parameters (e.g., power monitoring parameters)associated with the loads. In embodiments, the loads 111, 112, 113, 114,115 and IEDs 121, 122, 123, 124 may be installed in one or morebuildings or other physical locations or they may be installed on one ormore processes and/or loads within a building. The buildings maycorrespond, for example, to commercial, industrial or institutionalbuildings.

As shown in FIG. 1A, the IEDs 121, 122, 123, 124 are each coupled to oneor more of the loads 111, 112, 113, 114, 115 (which may be located“upstream” or “downstream” from the IEDs in some embodiments). The loads111, 112, 113, 114, 115 may include, for example, machinery orapparatuses associated with a particular application (e.g., anindustrial application), applications, and/or process(es). The machinerymay include electrical or electronic equipment, for example. Themachinery may also include the controls and/or ancillary equipmentassociated with the equipment.

In embodiments, the IEDs 121, 122, 123, 124 may monitor and, in someembodiments, analyze parameters (e.g., energy-related parameters)associated with the loads 111, 112, 113, 114, 115 to which they arecoupled. The IEDs 121, 122, 123, 124 may also be embedded within theloads 111, 112, 113, 114, 115 in some embodiments. According to variousaspects, one or more of the IEDs 121, 122, 123, 124 may be configured tomonitor utility feeds, including surge protective devices (SPDs), tripunits, active filters, lighting, IT equipment, motors, and/ortransformers, which are some examples of loads 111, 112, 113, 114, 115,and the IEDs 121, 122, 123, 124 may detect ground faults, voltage sags,voltage swells, momentary interruptions and oscillatory transients, aswell as fan failure, temperature, arcing faults, phase-to-phase faults,shorted windings, blown fuses, and harmonic distortions, which are someexample parameters that may be associated with the loads 111, 112, 113,114, 115. The IEDs 121, 122, 123, 124 may also monitor devices, such asgenerators, including input/outputs (I/Os), protective relays, batterychargers, and sensors (for example, water, air, gas, steam, levels,accelerometers, flow rates, pressures, and so forth).

According to another aspect, the IEDs 121, 122, 123, 124 may detectovervoltage and undervoltage conditions, as well as other parameterssuch as temperature, including ambient temperature. According to afurther aspect, the IEDs 121, 122, 123, 124 may provide indications ofmonitored parameters and detected conditions that can be used to controlthe loads 111, 112, 113, 114, 115 and other equipment in the electricalsystem in which the loads 111, 112, 113, 114 and IEDs 121, 122, 123, 124are installed. A wide variety of other monitoring and/or controlfunctions can be performed by the IEDs 121, 122, 123, 124, and theaspects and embodiments disclosed herein are not limited to IEDs 121,122, 123, 124 operating according to the above-mentioned examples.

It is understood that the IEDs 121, 122, 123, 124 may take various formsand may each have an associated complexity (or set of functionalcapabilities and/or features). For example, IED 121 may correspond to a“basic” IED, IED 122 may correspond to an “intermediate” IED, and IED123 may correspond to an “advanced” IED. In such embodiments,intermediate IED 122 may have more functionality (e.g., energymeasurement features and/or capabilities) than basic IED 121, andadvanced IED 123 may have more functionality and/or features thanintermediate IED 122. For example, in embodiments IED 121 (e.g., an IEDwith basic capabilities and/or features) may be capable of monitoringinstantaneous voltage, current energy, demand, power factor, averagesvalues, maximum values, instantaneous power, and/or long-duration rmsvariations, and IED 123 (e.g., an IED with advanced capabilities) may becapable of monitoring additional parameters such as voltage transients,voltage fluctuations, frequency slew rates, harmonic power flows, anddiscrete harmonic components, all at higher sample rates, etc. It isunderstood that this example is for illustrative purposes only, andlikewise in some embodiments an IED with basic capabilities may becapable of monitoring one or more of the above energy measurementparameters that are indicated as being associated with an IED withadvanced capabilities. It is also understood that in some embodimentsthe IEDs 121, 122, 123, 124 each have independent functionality.

In the example embodiment shown, the IEDs 121, 122, 123, 124 arecommunicatively coupled to a central processing unit 140 via the “cloud”150. In some embodiments, the IEDs 121, 122, 123, 124 may be directlycommunicatively coupled to the cloud 150, as IED 121 is in theillustrated embodiment. In other embodiments, the IEDs 121, 122, 123,124 may be indirectly communicatively coupled to the cloud 150, forexample, through an intermediate device, such as a cloud-connected hub130 (or a gateway), as IEDs 122, 123, 124 are in the illustratedembodiment. The cloud-connected hub 130 (or the gateway) may, forexample, provide the IEDs 122, 123, 124 with access to the cloud 150 andthe central processing unit 140.

As used herein, the terms “cloud” and “cloud computing” are intended torefer to computing resources connected to the Internet or otherwiseaccessible to IEDs 121, 122, 123, 124 via a communication network, whichmay be a wired or wireless network, or a combination of both. Thecomputing resources comprising the cloud 150 may be centralized in asingle location, distributed throughout multiple locations, or acombination of both. A cloud computing system may divide computing tasksamongst multiple racks, blades, processors, cores, controllers, nodes orother computational units in accordance with a particular cloud systemarchitecture or programming. Similarly, a cloud computing system maystore instructions and computational information in a centralized memoryor storage, or may distribute such information amongst multiple storageor memory components. The cloud system may store multiple copies ofinstructions and computational information in redundant storage units,such as a RAID array.

The central processing unit 140 may be an example of a cloud computingsystem, or cloud-connected computing system. In embodiments, the centralprocessing unit 140 may be a server located within buildings in whichthe loads 111, 112, 113, 114, 115, and the IEDs 121, 122, 123, 124 areinstalled, or may be remotely-located cloud-based service. The centralprocessing unit 140 may include computing functional components similarto those of the IEDs 121, 122, 123, 124 is some embodiments, but maygenerally possess greater numbers and/or more powerful versions ofcomponents involved in data processing, such as processors, memory,storage, interconnection mechanisms, etc. The central processing unit140 can be configured to implement a variety of analysis techniques toidentify patterns in received measurement data from the IEDs 121, 122,123, 124, as discussed further below. The various analysis techniquesdiscussed herein further involve the execution of one or more softwarefunctions, algorithms, instructions, applications, and parameters, whichare stored on one or more sources of memory communicatively coupled tothe central processing unit 140. In certain embodiments, the terms“function”, “algorithm”, “instruction”, “application”, or “parameter”may also refer to a hierarchy of functions, algorithms, instructions,applications, or parameters, respectively, operating in parallel and/ortandem. A hierarchy may comprise a tree-based hierarchy, such a binarytree, a tree having one or more child nodes descending from each parentnode, or combinations thereof, wherein each node represents a specificfunction, algorithm, instruction, application, or parameter.

In embodiments, since the central processing unit 140 is connected tothe cloud 150, it may access additional cloud-connected devices ordatabases 160 via the cloud 150. For example, the central processingunit 140 may access the Internet and receive information such as weatherdata, utility pricing data, or other data that may be useful inanalyzing the measurement data received from the IEDs 121, 122, 123,124. In embodiments, the cloud-connected devices or databases 160 maycorrespond to a device or database associated with one or more externaldata sources. Additionally, in embodiments, the cloud-connected devicesor databases 160 may correspond to a user device from which a user mayprovide user input data. A user may view information about the IEDs 121,122, 123, 124 (e.g., IED makes, models, types, etc.) and data collectedby the IEDs 121, 122, 123, 124 (e.g., energy usage statistics) using theuser device. Additionally, in embodiments the user may configure theIEDs 121, 122, 123, 124 using the user device.

In embodiments, by leveraging the cloud-connectivity and enhancedcomputing resources of the central processing unit 140 relative to theIEDs 121, 122, 123, 124, sophisticated analysis can be performed on dataretrieved from one or more IEDs 121, 122, 123, 124, as well as on theadditional sources of data discussed above, when appropriate. Thisanalysis can be used to dynamically control one or more parameters,processes, conditions or equipment (e.g., loads) associated with theelectrical system.

In embodiments, the parameters, processes, conditions or equipment aredynamically controlled by a control system associated with theelectrical system. In embodiments, the control system may correspond toor include one or more of the IEDs 121, 122, 123, 124 in the electricalsystem, central processing unit 140 and/or other devices within orexternal to the electrical system.

Referring to FIG. 1B, an example IED 200 that may be suitable for use inthe electrical system shown in FIG. 1A, for example, includes acontroller 210, a memory device 215, storage 225, and an interface 230.The IED 200 also includes an input-output (I/O) port 235, a sensor 240,a communication module 245, and an interconnection mechanism 220 forcommunicatively coupling two or more IED components 210-245.

The memory device 215 may include volatile memory, such as DRAM or SRAM,for example. The memory device 215 may store programs and data collectedduring operation of the IED 200. For example, in embodiments in whichthe IED 200 is configured to monitor or measure one or more electricalparameters associated with one or more loads (e.g., 111, shown in FIG.1A) in an electrical system, the memory device 215 may store themonitored electrical parameters.

The storage system 225 may include a computer readable and writeablenonvolatile recording medium, such as a disk or flash memory, in whichsignals are stored that define a program to be executed by thecontroller 210 or information to be processed by the program. Thecontroller 210 may control transfer of data between the storage system225 and the memory device 215 in accordance with known computing anddata transfer mechanisms. In embodiments, the electrical parametersmonitored or measured by the IED 200 may be stored in the storage system225.

The I/O port 235 can be used to couple loads (e.g., 111, shown in FIG.1A) to the IED 200, and the sensor 240 can be used to monitor or measurethe electrical parameters associated with the loads. The I/O port 235can also be used to coupled external devices, such as sensor devices(e.g., temperature and/or motion sensor devices) and/or user inputdevices (e.g., local or remote computing devices) (not shown), to theIED 200. The I/O port 235 may further be coupled to one or more userinput/output mechanisms, such as buttons, displays, acoustic devices,etc., to provide alerts (e.g., to display a visual alert, such as textand/or a steady or flashing light, or to provide an audio alert, such asa beep or prolonged sound) and/or to allow user interaction with the IED200.

The communication module 245 may be configured to couple the IED 200 toone or more external communication networks or devices. These networksmay be private networks within a building in which the IED 200 isinstalled, or public networks, such as the Internet. In embodiments, thecommunication module 245 may also be configured to couple the IED 200 toa cloud-connected hub (e.g., 130, shown in FIG. 1A), or to acloud-connected central processing unit (e.g., 140, shown in FIG. 1A),associated with an electrical system including IED 200.

The IED controller 210 may include one or more processors that areconfigured to perform specified function(s) of the IED 200. Theprocessor(s) can be a commercially available processor, such as thewell-known Pentium™, Core™, or Atom™ class processors available from theIntel Corporation. Many other processors are available, includingprogrammable logic controllers. The IED controller 210 can execute anoperating system to define a computing platform on which application(s)associated with the IED 200 can run.

In embodiments, the electrical parameters monitored or measured by theIED 200 may be received at an input of the controller 210 as IED inputdata, and the controller 210 may process the measured electricalparameters to generate IED output data or signals at an output thereof.In embodiments, the IED output data or signals may correspond to anoutput of the IED 200. The IED output data or signals may be provided atI/O port(s) 235, for example. In embodiments, the IED output data orsignals may be received by a cloud-connected central processing unit,for example, for further processing (e.g., to identify power qualityevents, as briefly discussed above), and/or by equipment (e.g., loads)to which the IED is coupled (e.g., for controlling one or moreparameters associated with the equipment, as will be discussed furtherbelow). In one example, the IED 200 may include an interface 230 fordisplaying visualizations indicative of the IED output data or signals.The interface 230 may correspond to a graphical user interface (GUI) inembodiments, and the visualizations may include tolerance curvescharacterizing a tolerance level of the equipment to which the IED 200is coupled, as will be described further below.

Components of the IED 200 may be coupled together by the interconnectionmechanism 220, which may include one or more busses, wiring, or otherelectrical connection apparatus. The interconnection mechanism 220 mayenable communications (e.g., data, instructions, etc.) to be exchangedbetween system components of the IED 200.

It is understood that IED 200 is but one of many potentialconfigurations of IEDs in accordance with various aspects of thedisclosure. For example, IEDs in accordance with embodiments of thedisclosure may include more (or fewer) components than IED 200.Additionally, in embodiments one or more components of IED 200 may becombined. For example, in embodiments memory 215 and storage 225 may becombined.

Returning now to FIG. 1A, in order to accurately describe aperiodicevents such as voltage sags in an electrical system (such as theelectric system shown in FIG. 1A), it is important to measure thevoltage signals associated with the event. Two attributes often used tocharacterize voltage sags and transients are magnitude (deviation fromthe norm) and duration (length in time) of the event. Both parametersare instrumental in defining, and thus, mitigating these types of powerquality issues. Scatter plots of an event's magnitude (y-axis) versusits corresponding duration (x-axis) are typically shown in a singlegraph called a “Magnitude-Duration” plot, “Power Tolerance Curve”, or asreferred to herein, a Tolerance Curve.

FIG. 2 illustrates a well-known Magnitude-Duration plot 250: theInformation Technology Industry (ITI) Curve (sometimes referred to as anITIC or CBEMA Curve) 260. The ITIC Curve 260 shows “an AC input voltageenvelope which typically can be tolerated (no interruption in function)by most Information Technology Equipment (ITE),” and is “applicable to120V nominal voltages obtained from 120V, 208Y/120V, and 120/240V 60Hertz systems.” The “Prohibited Region” in the graph includes any surgeor swell which exceeds the upper limit of the envelope. Events occurringin this region may result in damage to the ITE. The “No Damage Region”includes sags or interruptions (i.e., below the lower limit of theenvelope) that are not expected to damage the ITE. Additionally, the “NoInterruption in Function Region” describes the area between the bluelines where sags, swells, interruptions and transients can normally betolerated by most ITE.

As is known, constraints of the ITIC Curve 260 include:

-   -   1. It is a static/fixed envelope/curve,    -   2. It is proposed for IT,    -   3. It is intended for 120V 60 Hz electrical systems,    -   4. It is a standardized/generic graph describing what “normally”        should be expected,    -   5. It inherently provides no information regarding the        consequences of an event,    -   6. It is solely a voltage-based graph, and does not consider any        other electrical parameter(s), and    -   7. It is presented on a semi-log graph for multiplicative        efficiency.

It is understood that prior art tolerance curves such as the ITIC/CBEMA,SEMI Curve, or other manually configured curves are often nothing morethan suggestions for specific applications. They do not indicate how aspecific system or piece of equipment, apparatus, load, or controlsassociated with the equipment, apparatus, or load will actually respondto a sag/swell event, what the event's impact will be the electricalsystem, or how and where to economically mitigate the issues.Furthermore, zones (sub-systems) within the electrical system are alltreated the same, even though the majority of IEDs monitor multipleloads. A good analogy is a road atlas: while the atlas illustrates thelocation of the road, it does not indicate the location of road hazards,expected gas mileage, condition of the vehicle, construction, and soforth. A better approach is required to improve managing voltage sagsand swells in electrical systems.

With the foregoing in mind, the ability to provide customized tolerancecurves allows an energy consumer (and the systems and methods disclosedherein) to better manage their system through simplified investmentdecisions, reduced CAPEX and OPEX costs, identified and characterizedissues and opportunities, improved event ride-though, and ultimately,higher productivity and profitability.

A few example factors to be considered when leveraging the benefits ofproviding dynamic tolerance curves for energy consumers include:

-   -   1. No two customers are exactly alike, and no two metering        points are identical. A dynamic tolerance curve is uniquely        customized to the point at which the metering data is collected        on a specific electrical system.    -   2. As events occur and are captured, a dynamic tolerance curve        is continuously updated according the unique responses of the        electrical system.    -   3. A dynamic tolerance curve can be applied to any type of        electrical system/any type of customer; it is not limited to ITE        systems.    -   4. A dynamic tolerance curve can also be used for substantially        any voltage level; it is not limited to 120-volt systems.    -   5. A dynamic tolerance curve does not have fixed scaling; it can        (and may need to) auto-scale.    -   6. It is possible to automatically aggregate dynamic tolerance        curves from discrete devices into a single dynamic system        tolerance curve.

With the foregoing in mind, there are a plurality of new potentialfeatures according to this disclosure that can produce numerous benefitsfor energy consumers. In embodiments, the goal of these features is tosimplify a generally complex topic into actionable opportunities forenergy consumers. Example features according to this disclosure are setforth below for consideration.

I. Dynamic Tolerance Curves

This embodiment of the disclosure comprises automatically adjusting asag/swell tolerance curve based on load impact as measured by a discreteIED. In this embodiment, “load impact” is determined by evaluating apre-event load against a post-event load (i.e., the load after theevent's onset). The difference between the pre-event and post eventloads (i.e., kW, current, energy, etc.) is used to quantify the event'simpact. The measure of “impact” may be calculated as a percent value,absolute value, normalized value, or other value useful to the energyconsumer. Further evaluations may include changes in voltage, current,power factor, total harmonic distortion (THD) levels, discrete harmoniccomponent levels, total demand distortion (TDD), imbalance, or any otherelectrical parameter/characteristic that can provide an indication ofthe type (load or source), magnitude, and location of change within theelectrical system. The source of data may originate from logged data,waveform data, direct MODBUS reads, or any other means.

FIG. 3 illustrates a typical tolerance curve (e.g., ITIC curve), whichis used as a baseline (also shown in FIG. 2). It should be noted that inembodiments substantially any known uniquely described tolerance curve(e.g., SEMI F47, ANSI, CBEMA, other custom curve) may be used as thebaseline tolerance curve because an intent of this embodiment of thedisclosure is to dynamically customize (i.e., change, update, revise,etc.) the tolerance curve so that it reflects the unique electricalvoltage event tolerance characteristics at the IED's point ofinstallation. As more events are captured and quantified by the IED, theaccuracy and characterization of the dynamic voltage tolerance curve mayimprove at that IED's point of installation. FIG. 3 is also shown as asemi-logarithmic graph; however, the dynamic tolerance curve may bescaled in any practical format for both analyses and/or viewingpurposes.

FIG. 4 illustrates an example voltage sag event (50% of nominal, 3milliseconds duration) on a standard/baseline tolerance curve thatresults in the loss of 20% of the load as determined by the IED. Theshaded area in FIG. 5 illustrates the difference between the baselinetolerance curve (e.g., as shown in FIG. 3) and the actual tolerance ofthe downstream metered load(s) due to the particular sensitivity at thislocation in the electrical system to this degree (magnitude andduration) of voltage sag. FIG. 6 illustrates an example automaticallycustomized and updated tolerance curve built from the event data pointand determined for the point where the IED is installed on theelectrical system. In embodiments, it assumes that anysag/swell/transient event with more severe characteristics (i.e., deepervoltage sag, greater voltage swells, larger transient, longer duration,etc.) will impact the load at least as severely as the event presentlybeing considered.

FIG. 7 illustrates a multitude of voltage sags/swells/transients on astandard/baseline tolerance curve. Some events are indicated asimpactful and some are indicated as not impactful, based on one or morechanging parameters at the moment of the event. FIG. 8 illustrates anautomatically customized and updated tolerance curve for the multitudeof impactful and non-impactful voltage sags/swells/transients asdetermined by the measured data taken from the point where the IED isinstalled on the electrical system.

a. Three-Dimension (3-D) Dynamic Tolerance Curves with Load Impact (AlsoSometimes Referred to Herein as “Dynamic Tolerance-Impact Curves”)

Standard tolerance curves (e.g., ITIC Curve, SEMI Curve, etc.) aredescribed in two-dimensional graphs with percent of nominal voltage onthe y-axis and duration (e.g., cycles, second, milliseconds, etc.) onthe x-axis, for example, as shown in FIG. 7. While the y-axis ispresented in units of percent of nominal voltage, it is understood thatthe y-axis units may also be in absolute units (e.g., real values suchas voltage), or substantially any other descriptor of the y-axisparameter's magnitude. Additionally, while the x-axis is logarithmic inFIG. 7, for example, it is understood that the x-axis does not have tobe logarithmic (for example, it can be linear as well). These 2-Dstandard tolerance curve graphs provide only a limited description of anevent's characteristics (magnitude and duration); they don't provideinformation related to an event's impact on the load(s). While theenergy consumer knows an event occurred, they cannot tell whether (andif so, to what degree) an event impacted their electrical system (andlikely, their operation).

Adding a third dimension to the tolerance curve graph allows the energyconsumer to visually identify the characterization of their system'ssag/swell/transient tolerance (at the metering point) related tomagnitude, duration, and a third parameter such as load impact. Again,load impact is determined by analyzing changes in the load (or otherelectrical parameter) before and after an event using logged data,waveform data, direct MODBUS read data, other data, or any combinationthereof.

Three-dimensional (3-D) tolerance curves in accordance with embodimentsof the disclosure may be adapted and/or oriented to any axis,perspective, scale, numerically ascending/descending, alphabetized,color, size, shape, electrical parameter, event characteristic, and soforth to usefully describe an event or events to the energy consumer.For example, FIG. 9 illustrates an exemplary orthographic perspective ofa tolerance-impact curve incorporating three parameters: 1) percent ofnominal voltage on the y-axis, 2) duration in cycles and seconds on thex-axis, and 3) percent load impacted on the z-axis. While the y-axis ispresented in units of percent of nominal voltage in the illustratedembodiment, it is understood that the y-axis units may also be inabsolute units (e.g., real values such as voltage), or substantially anyother descriptor of the y-axis parameter's magnitude. Additionally,while the x-axis is logarithmic in the illustrated embodiment, it isunderstood that the x-axis does not have to be logarithmic (for example,it can be linear as well). FIG. 10 illustrates an exemplary single-pointperspective 3-D view of the same tolerance-impact curve shown in FIG. 9,and incorporates the same respective parameters for the three axes. Italso attempts to integrate color shading to help illustrate the severityof the impact due to specific magnitude and duration events (least toworst; yellow to red, in the illustrated embodiment). FIG. 11 attemptsto illustrate an exemplary single-point perspective 3-D view of atolerance-impact curve incorporating magnitude, duration, percent loadimpact, shading, and event shape (to provide more event characteristicsin a single graph). Again, the load impact may be as a relativepercentage of the total load before the event (as shown in the graph),as a real value (e.g., kW, Amps, etc.), ascending or descending invalue, or any other manipulation of these or any other electricalparameters.

b. Three-Dimension (3-D) Dynamic Tolerance-Recovery Time Curves

Building on the previous section discussing load impact, in embodimentsit is also possible to use tolerance-impact curves to more directlyquantify the effect of a voltage sag/swell/transient event on an energyconsumer's operation. The time to recover from an event may directlyaffect the overall cost of a voltage event.

For the purpose of this disclosure, “recovery time” is defined as theperiod of time required to return the electrical system parameters backto (or approximately back to) their original state prior to the eventthat prompted their initial perturbation. In embodiments, recovery timeand load impact are independent variables; neither is dependent on theother. For example, a voltage event may impact a small percentage ofload, yet the recovery time may be considerable. Conversely, therecovery time from an extremely impactful event could be relativelyshort. Just as the impact of an event is dependent on a number offactors (some examples of which are set forth in the summary section ofthis disclosure), so too is the recovery time. A few examples of factorsthat can heavily influence the duration of recovery time include:ability to quickly locate event source (if it's within the facility),extent of equipment damage, spare parts availability, personnelavailability, redundant systems, protection schemes, and so forth.

One example method for calculating the recovery time includes measuringthe elapsed time between the occurrence of a first impactful event andthe point when the load exceeds a predetermined threshold of thepre-event load. For example, a 500 kW pre-event load with a 90% recoverythreshold will indicate the recovery has occurred at 450 kW. If it takes26 minutes for the metered load to meet or exceed 450 kW (i.e., 90% ofthe pre-event load), then the recovery time is equal to 26 minutes. Therecovery threshold can be determined using a relative percentage of thepre-event load, an absolute value (kW), the recovery of the voltage orcurrent levels, an external or manual trigger, a recognized standardvalue, a subjective configuration, or by some other method using anelectrical or non-electrical parameter(s).

FIG. 12 illustrates an exemplary orthographic perspective of atolerance-recovery time curve incorporating three parameters: 1) percentof nominal voltage on the y-axis, 2) duration in cycles and seconds (oralternatively, milliseconds) on the x-axis, and 3) recovery time orperiod in days, hours, and/or minutes on the z-axis. While the y-axis ispresented in units of percent of nominal voltage in the illustratedembodiment, it is understood that the y-axis units may also be inabsolute units (e.g., real values such as voltage), or substantially anyother descriptor of the y-axis parameter's magnitude. Additionally,while the x-axis is logarithmic in the illustrated embodiment, it isunderstood that the x-axis does not have to be logarithmic (for example,it can be linear as well). In embodiments, the z-axis (recovery time)may be configured to substantially any fixed scale (or auto-scaled), maybe listed in ascending or descending order, and may use substantiallyany known temporal unit. FIG. 13 illustrates an exemplary single-pointperspective 3-D view of the same tolerance-recovery time curve shown inFIG. 12, and incorporates the same respective parameters for the threeaxes. FIG. 13 also integrates color shading to help illustrate theseverity of the recovery time due to specific magnitude and durationevents (least to worst; yellow to red in the illustrated embodiment).FIG. 14 illustrates an exemplary single-point perspective 3-D view of atolerance-recovery time curve incorporating magnitude, duration,recovery time, shading, and event shape (to provide more eventcharacteristics in a single graph).

c. 3-D Dynamic Tolerance-Economic Impact Curves

The 3-D curves discussed above may also be used to illustrate economicimpact (e.g., production losses, restart losses, product/materiallosses, equipment losses, third-party losses, total losses, etc.) as itrelates to voltage events. Obviously, configuration may betime-consuming; however, the relationship between recovery time and anyrelevant economic factor can easily be shown and understood usingdynamic tolerance-economic impact curves. The cost of downtime (CoD) maybe initially used to determine a given economic cost during the recoveryperiod (assuming the CoD value is reasonable). Some studies indicateeach minute of downtime costs energy consumers in the automotiveindustry more than $22K. By contrast, the similar studies indicate thathealthcare industry energy consumers lose more than $10K/minute ofdowntime. Over time, energy consumers (and the systems and methodsdisclosed herein) can quantify their typical recovery time costs(whether it's linear or non-linear), or they may have a study done todetermine this relationship at their facility or business. Determiningthe relationship between voltage events and economic factors will allowenergy consumers to make faster and better decisions regardingcapitalization expenditures and/or the retention of services.

For example, FIG. 15 illustrates the production losses with respect to a50% of nominal voltage sag event with a duration of 3 milliseconds.Assuming the recovery time was 8 hours (see, e.g., FIG. 13) andproduction losses are an average of $2.5K/hour, the total productionlosses will be $20K. If ride-through capabilities can help avoid anoperational disruption at a cost of $50K, the payback for investing involtage sag ride-though equipment is may only be about 2.5 voltageevents, for example. As mentioned at the beginning of this document,studies have shown the average industrial customer experiences about 66voltage sags each year so a decision to mitigate should bestraightforward in this case.

d. Upstream/Downstream Tolerance-Impact Curves

As has been stated and is widely known, electrical systems are sensitiveto voltage events in varying degrees. For some energy consumers, voltageevents may just be a nuisance (no significant impact); for other energyconsumers, any small voltage anomaly may be catastrophic. As previouslydiscussed, quantifying the impact of voltage events helps energyconsumers determine the severity, prevalence, and influence of theseevents on their operation. If voltage events impact the energyconsumer's operation, the next step is identifying the source of theproblem.

Metering algorithms and other associated methods may be used todetermine whether a voltage event's source is upstream or downstreamfrom a metering point (e.g., an IED's electrical point of installationin an electrical system). For example, FIG. 16 illustrates a simpleelectrical network with three metering points (M₁, M₂, and M₃). A fault(X) is shown to occur between M₁ and M₂. In embodiments, algorithms inM₁ may indicate the source of the fault to be downstream (↓) from itslocation, and algorithms in M₂ may indicate the source of the fault tobe upstream (↑) from its location. Additionally, in embodimentsalgorithms in M₃ may indicate the source of the fault to be upstream(↑). By evaluating the fault as a system event (i.e., using data fromall three IEDs), in embodiments it is possible to generally identify thelocation of the fault's source within the electrical network (i.e., withrespect to the metering points).

This embodiment evaluates the impact of a voltage event against theindicated location (upstream or downstream from the metering point)related to the voltage event's source. This is very useful becauseupstream voltage event sources often require different mitigativesolutions (and associated costs) than downstream voltage event sources.Furthermore, there will likely be different economic considerations(e.g., impact costs, mitigation costs, etc.) depending on where thevoltage event source is located within the electrical system. The largerthe impacted area, the more expensive the cost may be to mitigate theproblem. Upstream voltage events can potentially impact a larger portionof the electrical network than downstream voltage events, and therefore,may be more expensive to mitigate. Again, the cost to mitigate voltageevents will be determined on a case-by-case basis since each meteringpoint is unique.

In embodiments, the IEDs installed at the metering points are configuredto measure, protect, and/or control a load or loads. The IEDs aretypically installed upstream from the load(s) because current flow tothe load(s) may be a critical aspect in measuring, protecting and/orcontrolling the load(s). However, it is understood that the IEDs mayalso be installed downstream from the load(s).

Referring to FIG. 16A, another example electrical system includes aplurality of IEDs (IED1, IED2, IED3, IED4, IED5) and a plurality ofloads (L1, L2, L3, L4, L5). In embodiments, loads L1, L2 correspond to afirst load type, and loads L3, L4, L5 correspond to a second load type.The first load type may be the same as or similar to the second loadtype in some embodiments, or different from the second load type inother embodiments. Loads L1, L2 are positioned at a location that is“electrically” (or “conductively”) downstream relative to at least IEDsIED1, IED2, IED3 in the electrical system (i.e., IEDs IED1, IED2, IED3are upstream from loads L1, L2). Additionally, loads L3, L4, L5 arepositioned at a location that is “electrically” downstream relative toat least IEDs IED1, IED4, IED5 in the electrical system (i.e., IEDsIED1, IED4, IED5 are upstream from loads L3, L4, L5).

In the illustrated embodiment, a power quality event (or fault) X isshown occurring upstream relative to loads L1, L2. Up arrows indicate“upstream” and down arrows indicate “downstream” in the exampleembodiment shown. As illustrated, IEDs IED1, IED2 are shown pointingtowards the fault X. Additionally, IEDs IED3, IED4, IED5 are shownpointing upstream. In embodiments, this is because the path leading tothe fault X is upstream from IEDs IED3, IED4, IED5 respective locationin the electrical system, and downstream from IEDs IED1, IED2 respectivelocation in the electrical system. In embodiments, algorithms thatdetermine a direction of the fault X may be located (or stored) in theIEDs, on-site software, cloud-based systems, and/or gateways, etc., forexample.

FIG. 17 illustrates a 2-D graph voltage tolerance curve of voltageevents captured by an IED similar to FIG. 7 above; however, the upstreamand downstream voltage events are uniquely denoted andsuperimposed/overlaid together. FIG. 18 illustrates a 2-D voltagetolerance curve that shows only the upstream voltage events which aredisaggregated from the total set of voltage events shown in FIG. 17.Similarly, FIG. 19 illustrates a 2-D voltage tolerance curve showingonly the downstream voltage events as disaggregated from the total setof voltage events shown in FIG. 17. These graphs allow energy consumers(and the systems and methods disclosed herein) to distinguish theupstream events from the downstream events, thus, helping to provide abetter visually intuitive view for identifying the primary location ofvoltage event sources (and perhaps, their causes). Of course, additionalor alternative characteristics, parameters, filters, and/or otherrelated information (e.g., electrical data, time, metadata, etc.) may beused, displayed and/or plotted to further effectively and productivelyembellish the voltage tolerance curves.

For example, FIG. 20 illustrates an exemplary orthographic perspectiveof a tolerance-impact-source location curve incorporating fiveparameters: 1) percent of nominal voltage on the y-axis, 2) duration incycles and seconds on the x-axis, and 3) percent load impacted on thez-axis. While the y-axis is presented in units of percent of nominalvoltage in the illustrated embodiment, it is understood that the y-axisunits may also be in absolute units (e.g., real values such as voltage),or substantially any other descriptor of the y-axis parameter'smagnitude. Additionally, while the x-axis is logarithmic in theillustrated embodiment, it is understood that the x-axis does not haveto be logarithmic (for example, it can be linear as well). Additionaldimensions are also included in FIG. 20 such as the recovery time (sizeof data point) and whether a particular event was upstream or downstreamfrom the metering point (data point center is white or black,respectively). Moreover, the z-axis could be made to show the recoverytime while the size of the data point could be used to indicate thepercent load impacted. It is understood that many otherparameters/dimensions may be incorporated as makes sense and/or isuseful.

e. Mitigation of Sag/Swell/Transient Impact Using Dynamic ToleranceCurves

As noted above, electrical systems are typically sensitive to voltageevents in varying degrees. For some energy consumers, voltage events mayjust be a nuisance (no significant impact); for other energy consumers,any voltage event may be catastrophic. As previously discussed,quantifying the impact of voltage events helps energy consumersdetermine the severity, prevalence, and influence of these events ontheir operation. If voltage events have an impact the energy consumer'soperation, the next step should be identifying the problem so it can bereduced or eliminated altogether.

In embodiments, eliminating or reducing the impact of voltagesags/swells/transients (and momentary, temporary and instantaneousinterruptions) for the various embodiments discussed throughout thedisclosure, can be generally accomplished in three ways: 1) removing thesource of the voltage events, 2) reducing the number or severity ofvoltage events produced by the source, or 3) minimizing the effects ofthe voltage events on impacted equipment. In some embodiments, it issubstantially difficult to remove the source (or sources) of voltageevents because these same sources are usually an integral component orload within the facility's electrical infrastructure, process, and/oroperation. Additionally, the voltage event's source may be located onthe utility, and thus, hamper the ability to directly address a problem.If the voltage event's source is located inside the energy consumer'sfacility, it may be possible to minimize voltage events at the source byusing different techniques or technologies (e.g., “soft-start” motorsinstead of “across the line” motor starting). In some embodiments,removing or replacing the source (or sources) of voltage events maycost-prohibitive and require an extensive redesign of an electricalsystem or subsystem. It is also possible to “desensitize” equipmentagainst the effects of voltage events such as sags, swells, andtransients. As always, there are economic trade-offs when consideringthe best approach to reduce or eliminate voltage issues. FIG. 21 is agenerally recognized illustration showing the progression in cost tomitigate voltage events and other PQ-related issues, which tends toincrease as the solution moves closer to the source. A thorough economicevaluation may include both the initial and total life cycle costs for agiven solution. Furthermore, it may be very important to consider theresponse of any prospective solution to both internal and externalsources of system voltage perturbations.

As an example, motors are an important electrical apparatus used in mostprocesses. Standard (across the line) motor starts typically producevoltage sags due to the impedance between the source and motor and themotor's inrush current, which is typically 6-10 times the full-loadcurrent rating. Removing the motor from the process would most likely beimpractical; however, reducing the voltage sag or minimizing its effectson adjacent equipment may be viable alternatives. A few examplesolutions may include using different motor technologies such asvariable speed drives or to employ motor soft-start techniques tocontrol or limit the inrush current (and thus, reduce or eliminate thevoltage sag at start-up). Another example solution is to deploy one ormore of several mitigative devices or equipment to reduce the voltageevent's impact on sensitive equipment. Again, each electrical system isunique, so the cost to mitigate power quality disturbances may vary fromlocation to location, system to system, and customer to customer.

This embodiment includes evaluating the ride-through characteristics ofa multitude of mitigative devices against the dynamic tolerance-impactcurves provided by each capable IED. The output of the evaluation mayindicate the additional ride-through benefits of applying any particularmitigative device to any specific metering location. Moreover, acomparison of the economic, operational, and/or other benefits betweentwo or more ride-through technologies or techniques for a specificsystem or sub-system may also be provided. In embodiments, in order toperform the evaluation, a managed collection (or library) of mitigativedevices' ride-through characteristics may be assessed. The managedcollection (or library) of mitigative devices may include (but not belimited to) characteristics and/or capabilities such as type,technology, magnitude vs. duration behavior, load constraints, typicalapplications, purchase costs, installation costs, operational costs,availability, purchase sources, dimensions/form factors, brands, and soforth for each known variety. In embodiments, the characteristics andcapabilities described in the managed collection of mitigative deviceswill be considered (as required and as available) for application atevery (or substantially every) discretely metered point (or sub-system)where data is obtainable and assessible. One or more ride-throughcharacteristics curves (indicating magnitude vs. duration ride-throughcapabilities) for any or every mitigative device found in the managedcollection (library) may be superimposed/overlaid on the dynamictolerance curve for at least one or more discrete metering point(s).Alternatively, the evaluation may be provided through some other meansaccordingly. One or more characteristics and/or capabilities of themitigative device(s) may be included in the evaluation against thedynamic tolerance curve based on factors such as those listed andavailable in the managed collection (or library). In embodiments, thisevaluation may be alarm-driven, manually or automatically triggered,scheduled, or otherwise initiated.

The dynamic tolerance-impact curves provided by each capable IED for theelectrical system's hierarchy (or portions of its hierarchy) may beevaluated against the ride-through characteristics of one or moremitigative devices. In embodiments, it may be more feasible,cost-effective, or otherwise beneficial to provide ride-throughimprovements as part of a system, sub-system process, and/or discretelocation. Whereas it may be economical/practical/feasible to apply onetype of ride-though mitigative solution for one device or onesub-system/zone, it may be more economical/practical/feasible to providea different ride-through mitigative solution for another device orsubsystem/zone within the electrical system. In short, the mosteconomical/practical/feasible ride-through mitigative solution may beprovided for the entire (or portion of the) electrical system based onthe information available. In embodiments, other factors may beconsidered when determining ride-through improvements for one or morelocations within an electrical system; however, this applicationemphasizes the importance of leveraging discretely established dynamictolerance curves from one or more IEDs.

FIG. 22 illustrates the 2-D dynamic tolerance curve from FIG. 5. Again,this example shows a tolerance curve that has been customized andupdated based on a single 50% voltage sag lasting 3 milliseconds andhaving a 20% load impact. An evaluation may be performed to ascertainthe most economic/practical/feasible approach to improve theride-through performance for this particular location in the electricalsystem. The managed collection (library) of mitigative devices may beassessed against suitable options and viable solutions. FIG. 23 showsthe ride-through characteristics (magnitude vs. duration) of SagFighter®by Schneider Electric, which claims to meet SEMI F47,superimposed/overlaid on top of the updated dynamic tolerance curve.FIG. 24 provides the energy consumer with a graphical indication ofSagFighter's ride-through benefits at this particular location in theelectrical system (as indicated by the shaded area in FIG. 24, forexample). Of course, the final mitigation device recommendation providedto the energy consumer may be dependent on more than the ride-throughcharacteristic of the mitigative device (e.g.economical/practical/feasible/etc.). Additionally, this approach may beprovided to multiple metered points across the electrical system orsubsystems.

f. Determining Opportunity Costs for Ride-Through Mitigative SolutionsUsing Dynamic Tolerance Curves

As is known, opportunity cost refers to a benefit or gain that couldhave achieved, but was forgone in lieu of taking an alternative courseof action. For example, a facility manager with a fixed budget may beable to invest funds to expand the facility OR to improve thereliability of the existing facility. The opportunity cost would bedetermined based on the economic benefit of the choice not taken by thefacility manager.

In this embodiment of the disclosure, the “opportunity cost” is expandedto include other benefits such as production losses, material losses,recovery time, load impact, equipment losses, third-party losses, and/orany other loss that is quantifiable by some measure. Additionally, an“alternative course of action” may be the decision to take no action atall. A few benefits of taking no action include resource savings,monetary savings, time savings, reduced operational impact, deferral,and so forth. That is to say, decision-makers often consider thebenefits of taking no action greater than the benefits of takingspecific action(s).

The decision not to take an action is often based on the lack ofinformation related to a given problem. For example, if someone cannotquantify the benefits of taking a particular action, they are lesslikely to take that action (which may be the wrong decision).Conversely, if someone is able to quantify the benefits of taking aparticular action, they are more likely to make the right decision(whether to take action or not take action). Moreover, having qualityinformation available provides the tools to produce other economicassessments such as cost/benefit analyses and risk/reward ratios.

This embodiment of this disclosure may continuously (orsemi-continuously) evaluate the impact of voltage events(sags/swells/transients) against the ride-through tolerancecharacteristics of one or more mitigative devices, apparatuses and/orequipment. The evaluation may consider historical data to continuouslytrack voltage events, associated discrete and combined system impact(e.g., as a relative value, absolute value, demand, energy, or otherquantifiable energy-related characteristic), sub-system and/or systemperspective, hierarchical impact from two or more devices, zones,cross-zones, or combination thereof. Information taken from theevaluation may be used to provide feedback and metrics regarding theoperational repercussions that could have been avoided if one or moremitigative devices, apparatuses, and/or equipment would have beeninstalled at a location (or locations).

For example, FIG. 25 provides a 2-D graph that illustrates events (andany associated impacts) that could have been avoided (green circles) ifthe decision had been made to install SagFighter® prior to therespective voltage event. FIG. 26 illustrates a similar graph as shownin FIG. 25, but also includes the estimated recovery time that couldhave been avoided had mitigative solutions been implemented prior to thevoltage events. Metrics associated with these potentially avoided events(e.g., relative impact (%), absolute impact (W, kW, etc.), recovery timeper event, accumulated recovery time, downtime, losses, otherquantifiable parameters, etc.) may also be provided to an energyconsumer to help justify investments to resolve voltage sag issues. Theenergy consumer (or systems and methods of the disclosure herein) couldalso choose what level of mitigation would be justifiable by comparingdiffering mitigation techniques to the historical tolerance curve data(i.e., the point of diminishing region of interest (ROI)). Metrics maybe listed per event or accumulated, provided in a table or graphed,analyzed as a discrete point or from two or more devices (i.e., a systemlevel perspective), or otherwise manipulated to indicate and/or quantifythe impact and/or opportunity cost for not installing voltage eventmitigation. The same information could be displayed a 3-D orthographicperspective of a tolerance-impact curve incorporating at least threeparameters such as: 1) percent of nominal voltage on the y-axis, 2)duration in cycles and seconds on the x-axis, and 3) percent loadimpacted (or recovery time in days, hours or minutes) on the z-axis.While the y-axis is presented in units of percent of nominal voltage inthe illustrated embodiment, it is understood that the y-axis units mayalso be in absolute units (e.g., real values such as voltage), orsubstantially any other descriptor of the y-axis parameter's magnitude.Additionally, while the x-axis is logarithmic in the illustratedembodiment, it is understood that the x-axis does not have to belogarithmic (for example, it can be linear as well). Other parameters,characteristics, metadata, and/or mitigative apparatus may similarly beincorporated into a graph and/or table.

g. Verifying the Effectiveness of Mitigation Techniques Using DynamicTolerance Curves

Re-evaluating or reassessing the decision to invest in a facility'sinfrastructure is often overlooked, presumed, or merely based onspeculation and supposition. In most cases the benefits of installingmitigative technologies are just assumed, but never quantified.Measurement and Verification (M&V) processes focus on quantifying energysavings and conservation; however, steps to improve reliability andpower quality are not considered.

Embodiments of this disclosure periodically or continuously provide thefollowing example benefits:

-   -   Allocate risks between contractors and their customer (e.g., for        performance contracts),    -   Accurately assess voltage events to quantify savings (in impact,        recovery time, uptime, losses, or other economic factors),    -   Reduce voltage quality uncertainties to reasonable levels,    -   Aid in monitoring equipment performance,    -   Identify additional monitoring and/or mitigation opportunities,    -   Reduce impact to targeted equipment, and    -   Improve operations and maintenance.

The dynamic voltage-impact tolerance curve provides a baseline ofvoltage events at each discretely metered point that captures impactedor potentially impacted processes, operations or facilities.Post-installation evaluations may be performed using data taken from theareas predicted to experience the benefits. In embodiments, thesepost-installation evaluations compare “before vs. after” to quantify thereal benefits of installing the mitigative equipment. Determinedquantities may include reduced event impact, recovery time, operationalcosts, maintenance costs, or any other operational or economic variable.An exemplary equation to determine the calculated savings due toinstalling mitigative equipment may be:

Savings=(baseline costs−reduced downtime costs)±Adjustments

where “reduced downtime costs” may include all or some combination ofthe following:

-   -   Reduced production losses,    -   Reduced restart losses,    -   Reduced product/material losses,    -   Reduced equipment losses,    -   Reduced 3^(rd) party costs, and    -   . . . and/or some other loss reduction.

Installation costs for the mitigative equipment may need to beconsidered, likely as an “adjustment,” in some embodiments.

FIG. 27 illustrates an example 2-D dynamic voltage tolerance curveaccording to the disclosure where the blue threshold lines (-) representthe ride-through baseline thresholds at a discretely metered point andthe pink line (-) represents the predicted improvement to the voltageevent ride-through thresholds by installing a certain type of mitigationequipment. The green circles in FIG. 27 indicate the voltage events (andconsequently, the recovery time) expected to be avoided by installingthe mitigation equipment. FIG. 28 illustrates an example 2-D dynamicvoltage tolerance curve according to the disclosure showing the actualvoltage events and recovery time avoided due to the installation of themitigation equipment. The orange line (-) illustrates the actualimprovement to the voltage ride-through thresholds by installing themitigation equipment. In this example, the mitigation equipmentsurpassed its expectations by avoiding three additional voltage eventsand 22 hours (42 actual events-20 predicted events) of additionalrecovery time.

Each electrical system is unique and will perform differently to somedegree.

Embodiments of this disclosure use empirical data to characterize theactual performance of the mitigation equipment. For example, the actualthresholds for voltage ride-through (-) may perform better than expectedas shown in FIG. 28 because the downstream load on the mitigationequipment was/is less than expected. This allows the mitigation deviceto ride-through longer than anticipated. Conversely, exceeding themitigation equipment's load rating would likely result in aworse-than-expected performance. As the mitigation equipment's loadcontinues to be increased beyond its rating, the voltage ride-throughthresholds (-) will approach the original voltage ride-through threshold(-) or possibly be even more severe.

A 3-D dynamic tolerance curve similar to the one shown in FIG. 15 may beproduced to better demonstrate the effect of mitigation on otherparameters such as load impact, recovery time, economic factors, etc. Inthis case, at least three dimensions would be used to characterize theelectrical system at the point of the IED's installation. A 3-Devaluation would provide an even better intuitive understanding of amitigation equipment's historical, present and/or future performance. Itwould also make the selection of mitigation equipment for futureapplications less complicated and more cost-effective.

Metrics associated with the expected (based on historical data) andactually avoided events (e.g., relative impact (%), absolute impact (W,kW, etc.), reduced losses, other quantifiable parameters, etc.) may beprovided to an energy consumer to help justify the original oradditional investments to resolve voltage sag issues. Metrics may belisted per event or accumulated, provided in a table or graphed,analyzed as a discrete point or from two or more devices (i.e., a systemlevel perspective), or otherwise manipulated to indicate and/or quantifythe benefits and/or costs per avoided minute of impact due to theinstallation of voltage event mitigation. The same information could bedisplayed as a 3-D orthographic perspective of a tolerance-impact curveincorporating at least three parameters such as: 1) percent of nominalvoltage on the y-axis, 2) duration in cycles and seconds on the x-axis,and 3) percent load impacted (or recovery time) on the z-axis. While they-axis is presented in units of percent of nominal voltage in theillustrated embodiment, it is understood that the y-axis units may alsobe in absolute units (e.g., real values such as voltage), orsubstantially any other descriptor of the y-axis parameter's magnitude.Additionally, while the x-axis is logarithmic in the illustratedembodiment, it is understood that the x-axis does not have to belogarithmic (for example, it can be linear as well). Other parameters,characteristics, metadata, and/or mitigative apparatus could similarlybe incorporated into a graph and/or table, for example.

II. Using Non-PQ IEDs to Help Quantify Voltage Event Impact

The ability to quantify the impact of a voltage event may be derivedfrom measured changes in energy, current, or power flows (i.e.,consumption). An IED may be used to provide these measurements. Themeasurements may be acquired in real-time (e.g., via direct MODBUSreads), historically (e.g., logged data), or by some other means.

Power monitoring systems often incorporate a diverse array of IEDs thatare installed throughout the energy consumer's electrical system. TheseIEDs may have different levels of capabilities and feature sets; somemore and some less. For example, energy consumers often install high-end(many/most capabilities) IEDs at the location where electrical energyenters their premises (M₁ in FIG. 29). This is done to acquire thebroadest understanding possible of the electrical signals' quality andquantity as received from the source (typically, the utility). Becausethe budget for metering is usually fixed and the energy consumer oftenwants to meter as broadly as possible across their electrical system,conventional wisdom stipulates using IEDs with progressively lowercapabilities as the installed meter points get closer to the loads (seeFIG. 29, for example). In short, the majority of facilities incorporatemany more low/mid-range IEDs than high-end IEDs.

“High-end” metering platforms (and some “mid-range” metering platforms)are more expensive and generally capable of capturing PQ phenomenaincluding high-speed voltage events. “Low-end” metering platforms areless expensive and generally have reduced processor bandwidth, samplerates, memory, and/or other capabilities as compared to high-end IEDs.The emphasis of low-end IEDs, including energy measurements taken inmost breakers, UPSs, VSDs, etc., is typically energy consumption orother energy-related functions, and perhaps some very basic PQ phenomena(e.g., steady-state quantities such as imbalance, overvoltage,undervoltage, etc.).

This feature leverages (i.e., interrelates, correlates, aligns, etc.)one or more voltage event indicators, statistical derivations and/orother information from a high-end IED with one or more similar and/ordisparate measured parameters from a low-end IED with the goal ofquantifying the impact, recovery time, or other event characteristic atthe low-end IED. One exemplary method to do this is by using the voltageevent timestamp (indicator of the moment a voltage event occurs) fromthe high-end IED as a reference point for evaluating a measurableparameter corresponding with the same timestamp at a low-end that doesnot inherently have the capability to capture voltage events. Dataevaluated at both the high-end, mid-range, and low-end IEDs may include(but not be limited to) the event magnitude, duration, phase or linevalues, energy, power, current, sequential components, imbalance,timestamp, pre/during/post-event changes, any other measured orcalculated electrical parameter, metadata, meter characteristics, and soforth. Again, the measurements may be acquired in real-time (e.g., viadirect MODBUS reads), historically (e.g., logged data), or by some othermeans.

Another example way to leverage non-PQ IEDs is to extend the use ofevent alarms (including voltage events) derived from high-end IEDs. Forexample, when a high-end IED detects a voltage event, coincident datafrom low-end IEDs is analyzed to ascertain the impact, recovery time, orother event characteristic and/or parameter. If analysis of data fromthe low-end IED indicates some level of impact did occur, a voltageevent alarm, impact alarm, and/or other alarm type may be generated bythe system performing the analysis of the coincident data. The alarminformation may include any relevant parameter and/or information asmeasured by the low-end IED, high-end IED, metadata, metercharacteristics, load impact, recovery time, which one or more high-endIEDs triggered the low-end IED alarm, and so forth.

FIGS. 29 and 30 illustrate a relatively simple example of thisembodiment of the disclosure. At time to, a high-end IED installed arespective metering point or location M₁ indicates the beginning of avoltage event. The pre-event load is measured, and the recovery timeclock begins for the IED installed at the metering location M₁. Otherrelevant data, metrics and/or statistically derived information may alsobe measured or calculated as required. Simultaneously, the software(on-site and/or cloud-based) and/or hardware managing the meteringsystem evaluates the other connected IEDs to determine whether any otherIED installed at another respective metering point or location (e.g.,M₂, M₃, M₄, M₅, M₆, M₇, M₈, M₉, M₁₀) concurrently experienced animpactful event. In this example, the IED installed at metering locationM₇ is found to have experienced a coincident impactful event (the otherdevices are ignored in this example for the sake of simplicity). Thepre-event load is determined from M₇ and the recovery time clock beginsfor M₇ using the voltage event's timestamp as a reference. With the IEDsinstalled at metering locations M₁ and M₇ identified as impacted by thevoltage event, the impact is quantified based on pre/during/post-eventelectrical parameters (e.g., power, current, energy, voltage, etc.) withto derived from the IED installed at metering location M₁ and used as areference point for both devices M₁ and M₇. The IED installed atmetering location M₇ is located downstream from the IED installed atmetering location M₁ and experiences a more significant relative impact(i.e., bigger percentage of its pre-event load) due to system impedanceand uniquely affected loads. The recovery time counters for the IEDsinstalled at metering locations M₁ and M₇ are independent of each other,as will be the case for all IEDs. In this example, the IED installed atmetering location M₇ experiences approximately the same recovery time asthe IED installed at metering location M₁ (i.e., t_(M1r)≈t_(M7r));however, that may not always be the case since recovery time may beunique at each metered location.

In embodiments, virtual metering may also be used to identify an impactof a voltage event on unmetered loads. For example, there are twoelectrical paths downstream from the IED installed at metering locationM₁ in FIG. 30A. The electrical path on the right is metered through aphysical IED (e.g., installed at metering location M₂); however, theelectrical path on the left is not directly metered by a physical IED.If the load data measured by the IEDs installed at metering locations M₁and M₂ are measured synchronously or pseudo-synchronously, it ispossible to determine (within the accuracy and synchronizationconstraints of the IEDs installed at metering locations M₁ and M₂) theload flowing through the unmetered path, V₁, by the following equation:V₁=M₁-M₂. V₁ represents a location of a “virtual meter” or a “virtualIED” in the electrical system, and it signifies the difference betweenthe IEDs installed metering locations M₁ and M₂ for any synchronous (orpseudo-synchronous) load measurement.

For this example, consider a fault that occurs downstream from the IEDinstalled at metering location M₁ and upstream from the virtual meterlocated at metering location V₁ in FIG. 30A. Using the concept ofvirtual metering as described above, a load change is determined to haveoccurred in the unmetered path. Because the load data through theunmetered path may be derived from the IEDs installed at meteringlocations M₁ and M₂, it is possible to calculate the load impact to theunmetered path due to the fault. In this example, other importantparameters related to this embodiment of the disclosure may also bederived from virtual meters including recovery time, economic impact,and so forth.

In one embodiment, the data sample rate (e.g., power, current, energy,voltage, or other electrical parameters) for IEDs installed at meteringlocations M₁, M₇, and/or any other IEDs may be dependently orindependently increased as required after a voltage event has beenindicated in order to provide more accurate results (e.g., recoverytime). Data may be shown in a tabular format, graphically in 2-D or 3-D,color coded, as timelines from discrete IEDs, zonally, hierarchically,or as a system (aggregated) view, linearly or logarithmically, or in anyother structure or method considered relevant and/or useful. The outputof this embodiment may be via report, text, email, audibly,screen/display, or by some other interactive means.

Referring to FIGS. 30B-30I, several example figures are provided tofurther illustrate the concept of virtual metering in accordance withembodiments of this disclosure. As discussed above, an electrical systemtypically includes one or more metering points or locations. As alsodiscussed above, one or more IEDs (or other meters, such as virtualmeters) may be installed or located (temporarily or permanently) at themetering locations, for example, to measure, protect, and/or control aload or loads in the electrical system.

Referring to FIG. 30B, an example electrical system including aplurality of metering locations (here, M₁, M₂, M₃) is shown. In theillustrated embodiment, at least one first IED is installed at the firstmetering location M₁, at least one second IED is installed at the secondmetering location M₂, and at least one third IED is installed at thethird metering location M₃. The at least one first IED is a so-called“parent device(s),” and the at least one second IED and the at least onethird IED are so-called “child devices.” In the example embodimentshown, the at least one second IED and the at least one third IED arechildren of the at least one first IED (and, thus siblings with eachother), for example, due to the at least one second IED and the at leastone third IED both being installed at respective metering locations M₂,M₃ in the electrical system that “branch” from a common point (here,connection 1) associated with the metering location M₁ at which the atleast one first IED is installed. Connection 1 is the physical point inthe electrical system where the energy flow (as measured at M₁ by the atleast one first IED) diverges to provide energy to the left and rightelectrical system branches (as measured at M₂ and M₃ by the at least onesecond IED and the at least one third IED, respectively).

The electrical system shown in FIG. 30B is an example of a “completelymetered” system, where all branch circuits are monitored by a physicalIED (here, the at least one first IED, the at least one second IED, andthe at least one third IED). In accordance with various aspects of thisdisclosure, dynamic tolerance curves can be independently developed foreach discrete metered location (M₁, M₂, M₃) without any dependence onexternal input(s) from other IEDs. For example, electrical measurementdata from energy-related signals captured by the at least one first IEDinstalled at the first metering location M₁ may be used to generate aunique dynamic tolerance curve for the metering location M₁ (e.g., asshown in FIG. 30C) without any input (or data) from the at least onesecond IED or the at least one third IED. Additionally, electricalmeasurement data from energy-related signals captured by the at leastone second IED installed at the second metering location M₂ may be usedto generate a unique dynamic tolerance curve for the metering locationM₂ (e.g., as shown in FIG. 30D) without any input (or data) from the atleast one first IED or the at least one third IED. Further, electricalmeasurement data from energy-related signals captured by the at leastone third IED installed at the third metering location M₃ may be used togenerate a unique dynamic tolerance curve for the metering location M₃(e.g., as shown in FIG. 30E) without any input (or data) from the atleast one first IED or the at least one second IED.

Referring to FIG. 30F, in which like elements of FIG. 30B are shownhaving like reference designations, another example electrical system isshown. Similar to the electrical system shown in FIG. 30B, theelectrical system shown in FIG. 30F includes a plurality of meteringlocations (here, M₁, M₂, V₁). Also, similar to the electrical systemshown in FIG. 30B, the electrical system shown in FIG. 30F includes atleast one metering device installed or located at each of the meteringlocations (M₁, M₂). Here, however, unlike the electrical system shown inFIG. 30B, the electrical system shown in FIG. 30F includes a virtualmeter (V₁) accordance with embodiments of this disclosure.

In the illustrated embodiment, at least one first IED is installed at afirst “physical” metering location M₁, at least one second IED isinstalled at a second “physical” metering location M₂, and at least onevirtual meter is derived (or located) at a “virtual” (non-physical)metering location V₁. The at least one first IED is a so-called “parentdevice” and the at least one second IED and the at least one virtualmeter are so-called “child devices”. In the example embodiment shown,the at least one second IED and the at least one virtual meter arechildren of the at least one first IED (and, thus considered to besiblings with each other). In the illustrated embodiment, the at leastone second IED and the at least one virtual meter are installed andderived, respectively, at respective metering locations M₂, V₁ in theelectrical system that “branch” from a common point (here, connection 1)associated with the metering location M₁ at which the at least one firstIED is installed. Connection 1 is the physical point in the electricalsystem where the energy flow (as measured at M₁ by the at least onefirst IED) diverges to provide energy to the left and right branches (asmeasured at M₂ by the at least one second IED, and as calculated for V₁by the at least one virtual meter).

In accordance with embodiments of this disclosure, electricalmeasurement data associated with the virtual metering location V₁ may becreated/derived by calculating the difference between the synchronous(or pseudo-synchronous) data from the at least one first IED (here, aparent device) installed at the first metering location M₁ and the atleast one second IED (here, a child device) installed at the secondmetering location M₂. For example, electrical measurement dataassociated with the virtual metering location V₁ may be derived bycalculating the difference between electrical measurement data fromenergy-related signals captured by the at least one first IED andelectrical measurement data from energy-related signals captured by theat least one second IED, at a specific point in time (e.g., V₁=M₁−M₂,for synchronous or pseudo-synchronous data). It is understood thatvirtual meters (e.g., the at least one virtual meter located at virtualmetering location V₁) may include data from one or more unmetered branchcircuits, which are inherently aggregated into a single representativecircuit.

The electrical system shown in FIG. 30F is an example of a “partiallymetered” system, where only a subset of the total circuits is monitoredby physical IEDs (here, the at least one first IED and the at least onesecond IED). In accordance with various aspects of this disclosure,dynamic tolerance curves can be independently developed for eachphysically metered point (M₁, M₂) without any dependence on externalinput(s) from other IEDs. Additionally, in accordance with variousaspects of this disclosure, the dynamic tolerance curve for a virtuallymetered point (V₁) is derived using select synchronous (orpseudo-synchronous) and complementary data (e.g., power, energy,voltage, current, harmonics, etc.) from physical IEDs (here, the atleast one first IED, and the at least one second IED), and is dependent(sometimes, completely dependent) on these devices (here, the at leastone first IED, and the at least one second IED). For example, returningbriefly to FIGS. 30C-30E, the dynamic tolerance curve for virtualmetered point V₁ may be derived from the dynamic tolerance curve datafor physical metered points M₁, M₂ (e.g., as shown in FIGS. 30C and 30D,respectively). Because of this dependency, it is understood that issues(e.g., accuracy, missing data, non-synchronous data, etc.) with the atleast one first IED and the at least one second IED will be reflected inthe resulting virtual meter data in the illustrated embodiment. In theillustrated embodiment, the dynamic tolerance curve for virtual meteredpoint V₁ may be the same as (or similar to) the dynamic tolerance curveshown in FIG. 30E as an example.

Referring to FIG. 30G, a further example electrical system includes atleast one virtual meter located at a “virtual” metering location V₁, atleast one first IED installed at a first “physical” metering locationM₁, and at least one second IED installed at a second “physical”metering location M₂. The at least one virtual meter is a so-called“parent device” or “virtual parent device,” and the at least one firstIED and the at least one second meter are “child devices.” In theexample embodiment shown, the at least one first IED and the at leastone second IED are children of the at least one virtual meter (and, thusconsidered to be siblings with each other).

As illustrated, the at least one first IED and the at least one secondIED are both installed (or located) at respective metering locations M₁,M₂ in the electrical system that “branch” from a common point (here,connection 1) associated with the virtual metering location V₁ at whichthe at least one virtual meter is derived (or located). Connection 1 isthe physical point in the electrical system where the energy flow (ascalculated at V₁) diverges to provide energy to the left and rightbranches (as measured at M₁ and M₂ by the at least one first IED and theat least one second IED, respectively).

In accordance with embodiments of this disclosure, electricalmeasurement data associated with the first metering location V₁ iscreated/derived through a slightly different approach than describedabove in connection with FIG. 30F, for example. In particular, theelectrical measurement data associated with the first metering locationV₁ may be determined by calculating the summation of synchronous (orpseudo-synchronous) data from the at least one first child IED installedat metering location M₁ and the at least one second child IED deviceinstalled at metering location M₂ (e.g., V₁=M₁+M₂, for synchronous orpseudo-synchronous data).

The electrical system shown in FIG. 30G is an example of a “partiallymetered” system, where only a subset of the total circuits is monitoredby physical IEDs. In accordance with various aspects of this disclosure,dynamic tolerance curves can be independently developed for eachphysically metered point (M₁, M₂) without any dependence on externalinput(s) from other IEDs. Additionally, in accordance with variousaspects of this disclosure, the dynamic tolerance curve for the virtualparent meter (V₁) is derived using select complementary data (e.g.,power, energy, voltage, current harmonics, etc.) from physical IEDs (M₁,M₂), and is completely dependent on these devices (M₁, M₂). Because ofthis dependency, it is understood that any issue (e.g., accuracy,missing data, non-synchronous data, etc.) with meters M₁ and M₂ will bereflected in virtual parent device V₁.

Referring to FIG. 30H, another example electrical system includes atleast one first virtual meter located at a first “virtual” meteringlocation V₁, at least one first IED installed at a first “physical”metering location M₁, and at least one second virtual meter installed ata second “virtual” metering location V₂. The at least one virtual meteris a “parent device” or a “virtual parent device”, and the at least onefirst IED and the at least one second virtual meter are “child devices”.In the example embodiment shown, the at least one first IED and the atleast one second virtual meter are children of the at least one firstvirtual meter (and, thus considered to be siblings with each other).

As illustrated, the at least one first IED and the at least one secondvirtual meter are installed and derived, respectively, at respectivemetering locations M₁, V₂ in the electrical system that “branch” from acommon point (here, connection 1) associated with the first virtualmetering location V₁ at which the at least first one virtual meter islocated (or derived). Connection 1 is the physical point in theelectrical system where the energy flow (as calculated at V₁) divergesto provide energy to the left and right branches (as measured at M₁ bythe at least one first IED, and as calculated at V₂).

In accordance with some embodiments of this disclosure, the electricalsystem shown in FIG. 30H is mathematically and probabilisticallyindeterminate because there are too many unknown values from necessaryinputs. Assumptions may be made regarding the occurrence of powerquality events (e.g., voltage events) on the virtual devices (V₁, V₂);however, the impact of the power quality events may impossible (orextremely hard) to define in this case. As is appreciated fromdiscussions above and below, virtual metering data is derived from datataken from physical IEDs. In the embodiment shown in FIG. 30H, there aretoo few physical IEDs to derive the “virtual” data. FIG. 30H is shown toillustrate some constraints related to virtual IED derivations.

Referring to FIG. 30I, a further example electrical system includes atleast four virtual meters (or IEDs) located (or derived) at respective“virtual” metering locations (V₁, V₂, V₃, V₄) in the electrical system,and at least five IEDs installed at respective “physical” meteringlocations (M₁, M₂, M₃, M₄, M₅) in the electrical system. In particular,the electrical system includes at least one first “parent” virtual meterlocated at a first “virtual” metering location V₁, at least one first“child” IED installed at a first “physical” metering location M₁, and atleast one second “child” IED installed at a second “physical” meteringlocation M₂ (with the at least one first IED at metering location Wandthe at least one second IED at metering location M₂ being children ofthe at least one first virtual meter at metering location/position V₁).The electrical system also includes at least one third “child” IEDinstalled at a third “physical” metering location M₃ and at least onesecond “child” virtual meter located at a second “virtual” meteringlocation V₂ (with the at least one third IED at metering location M₃ andthe at least one second virtual meter at metering location V₂ beingchildren of the at least one first IED at metering location M₁).

The electrical system further includes at least one fourth “child” IEDinstalled at a fourth “physical” metering location M₄ and at least onethird “child” virtual meter located at a third “virtual” meteringlocation V₃ (with the at least one fourth IED at metering location M₄and the at least one third virtual meter at metering location V₃ beingchildren of the at least one second virtual meter at metering locationV₂). The electrical system also includes at least one fifth “child” IEDinstalled at a fifth “physical” metering location M₅ and at least onefourth “child” virtual meter located at a fourth “virtual” meteringlocation V₄ (with the at least one fifth IED at metering location M₅ andthe at least one fourth virtual meter at metering location V₄ beingchildren of the at least one third virtual meter at metering locationV₃). As illustrated, there are essentially five layers in the meteringhierarchy from the first virtual metering location V₁, to the fifth“physical” metering location M₅ and the fourth “virtual” meteringlocation V₄.

The electrical system shown in FIG. 30I illustrates a partially meteredsystem, where only a subset of the total circuits is monitored byphysical devices/IEDs. In accordance with various aspects of thisdisclosure, dynamic tolerance curves can be independently developed foreach physically metered location (M₁, M₂, M₃, M₄, M₅) without anydependence or interdependence on external input(s) from other IEDs. Thedynamic tolerance curves for the virtual metering locations V₁, V₂, V₃,V₄ may be derived from complementary and synchronous (orpseudo-synchronous) data (e.g., power, energy, voltage, current,harmonics, etc.) as measured by physical IEDs installed at thediscretely metered locations M₁, M₂, M₃, M₄, M₅. Additionally,electrical measurement data from energy-related signals captured by theat least one second IED installed at the second metering location M₂ maybe used to generate a dynamic tolerance curve for the metering locationM₂ without any input (or data) from the at least one first IED or the atleast one third IED.

In particular, the electrical measurement data associated with the firstvirtual metering location V₁ may be determined (and used to helpgenerate a dynamic tolerance curve for the first virtual meteringlocation V₁) by calculating the summation of synchronous (orpseudo-synchronous) data from the at least one first child IED installedat metering location M₁ and the at least one second child IED deviceinstalled at metering location M₂ (e.g., V₁=M₁+M₂, for synchronous orpseudo-synchronous data). Additionally, the electrical measurement dataassociated with the second metering location V₂ may be determined (andused to help generate a dynamic tolerance curve for the second virtualmetering location V₂) by calculating the difference between synchronous(or pseudo-synchronous) data from the at least one first child IEDinstalled at metering location M₁ and the at least one third child IEDdevice installed at metering location M₃ (e.g., V₂=M₁-M₃, forsynchronous or pseudo-synchronous data).

The electrical measurement data associated with the third virtualmetering location V₃ may be determined (and used to help generate adynamic tolerance curve for the third virtual metering location V₃) byfirst calculating the difference between synchronous (orpseudo-synchronous) data from the at least one first child IED installedat metering location M₁ and the at least one third child IED deviceinstalled at metering location M₃, and then calculating the differencebetween the first calculated difference and synchronous (orpseudo-synchronous) data from the at least one fourth child IEDinstalled at metering location M₄ (e.g., V₃=M₁-M₃-M₄, for synchronous orpseudo-synchronous data).

Additionally, the electrical measurement data associated with the fourthvirtual metering location V₄ may be determined (and used to helpgenerate a dynamic tolerance curve for the fourth virtual meteringlocation V₄) by first calculating the difference between synchronous (orpseudo-synchronous) data from the at least one first child IED installedat metering location M₁ and the at least one third child IED deviceinstalled at metering location M₃, and then calculating the differencebetween the synchronous (or pseudo-synchronous) data from the at leastone fourth child IED installed at metering location M₄ and the at leastone fifth child IED installed at metering location M₅. The differencebetween the first calculated difference and the calculated differencebetween the synchronous (or pseudo-synchronous) data from the at leastone fourth child IED installed at metering location M₄ and the at leastone fifth child IED installed at metering location M₅ may be used todetermine the electrical measurement data associated with the fourthvirtual metering location V₄ (e.g., V₄=M₁-M₃-M₄-M₅, for synchronous orpseudo-synchronous data).

As will be further appreciated from discussions below, using eventtriggers or alarms from one or more of the physical IEDs (M₁, M₂, M₃,M₄, M₅), it is possible to use pre-event and post-event data from thephysical IEDs to develop dynamic tolerance curves, determine eventimpacts, quantify recovery times, and assess other associated costs atthe virtual meters (and metering locations V₁, V₂, V₃, V₄). Again,validity of the derived information for the virtual meter (V₁, V₂, V₃,V₄) is dependent on the veracity, accuracy, synchronicity, andavailability of data from the physical IEDs (M₁, M₂, M₃, M₄, M₅). Inthis particular case, there are many interdependencies used to derivedata for the virtual meters (and metering locations V₁, V₂, V₃, V₄), soit is understood that some deficiency may be experienced for one or morederivations.

It is understood that the above-described examples for determining,deriving, and/or generating dynamic tolerance curves for virtual metersin an electrical system may also apply to aggregation of zones andsystems. In spirit of the concepts describing “operational impact,”“recovery time,” “recovery energy costs,” and so forth, it is understoodthat aggregation may only make sense when it is 1) directly useful tothe customer/energy consumer, 2) and/or useful to be leveraged foradditional customer and/or business-centered benefits (present orfuture). That is why the best approach to aggregation is typically tofocus on the worst-case scenario (i.e., event impact, event recoverytime, other associated event costs, etc.). If aggregation is performedand it does not reflect the customers experience in trying to resolvethe event in question, then it is difficult to achieve any usefulnessfrom the aggregation. In short, just because something is mathematicallyand/or statistically feasible does not necessarily make it useful.

III. Evaluating Load Impact and Recovery Time Using Hierarchy andDynamic Tolerance Curve Data

In embodiments, when a load impacting voltage event occurs, it isimportant for the energy consumer (or the systems and methods disclosedherein) to prioritize the “what, when, why, where, who, how/how much/howsoon, etc.” of the response. More specifically: 1) what happened, 2)when did it happen, 3) why did it happen, 4) where did it happen, 5)who's responsible, 6) how do I resolve the issue, 7) how much is itgoing to cost, and 8) how soon can I get it resolved. Embodimentsdescribed herein assist energy consumers with answering these questions.

Understanding and quantifying the impact of voltage (and/or other)events from a IED, zone, and/or system perspective is extremelyimportant for energy consumers to understand their electrical system andfacility's operation in its entirety, and to respond to electricalevents accordingly. Because each load has unique operatingcharacteristics, electrical characteristics and ratings, functions, andso forth, the impact of a voltage event may differ from one load to thenext. This can result in unpredictable behavior, even with comparableloads connected to the same electrical system and located adjacent toeach other. It is understood that some aspects of the embodimentsdescribed below may refer to or overlap with previously discussed ideaspresented herein.

System (or hierarchical) perspectives show how an electrical system ormetering system is interconnected. When a voltage event occurs, itsimpact is strongly influenced by the system impedance and sensitivity ofa given load. For example, FIG. 31 illustrates a relatively simplefully-metered electrical system experiencing a voltage event (e.g., dueto a fault). In general, the system impedance will dictate the magnitudeof the fault, protective devices will dictate the duration of the fault(clearing time), and location of the fault will be an important factorin the scope of the fault's impact to the electrical system. In FIG. 31,its possible (even likely) the shaded area will experience a significantvoltage sag followed by an interruption (due to the operation ofprotective device(s)). In embodiments, the duration of the event'simpact will be from the onset time of the fault until the system isagain operating normally (note: this example states a recovery time of 8hours). The unshaded area of the electrical system in FIG. 31 may alsoexperience a voltage event due to the fault; however, the recovery timefor the unshaded area will likely be briefer than the shaded area.

In embodiments, both the shaded and unshaded areas of the electricalsystem shown in FIG. 31 may be impacted by the fault; however, both mayexhibit different recovery time durations. If the processes served byboth the shaded and unshaded areas are critical to the facility'soperation, then the system recovery time will be equal to the greater ofthe two recovery times.

In embodiments, it is important to identify and prioritize IEDs, zones,and/or systems. Zones may be determined within the electrical systemhierarchy based on: protection schemes (e.g., each breaker protects azone, etc.), separately derived sources (e.g., transformers, generators,etc.), processes or sub-systems, load types, sub-billing groups ortenants, network communications schemes (e.g., IP addresses, etc.), orany other logical classification. Each zone is a subset of the meteringsystem's hierarchy, and each zone may be prioritized by type and eachzone may be assigned more than one priority if applicable (e.g., highpriority load type with low priority process). For example, if aprotective device also acts as a IED and is incorporated into themetering system, it and the devices below it could be considered a zone.If the protective devices are layered in a coordinated scheme, the zoneswould be similarly layered to correspond with the protective devices. InFIG. 32, another method to automatically determine zones involvesleveraging hierarchical context to evaluate voltage, current, and/orpower data (other parameters may also be used as necessary) to identifytransformer locations. FIG. 32 indicates three zones: utility source,transformer 1, and transformer 2. FIG. 33 is an exemplary illustrationof an energy consumer's custom zone configuration.

Once the zones are established, prioritizing each zone will help theenergy consumer better respond to voltage events (or any other event)and their impact. While there are techniques to automatically prioritizezones (e.g., largest to smallest load, load types, recovery times,etc.), the most prudent approach would be for the energy consumer torank the priorities of each zone. It is certainly feasible (andexpected) for two or more zones to have an equal ranking in priority.Once zone priorities are established, it is then possible to analyze theload impact and recovery time for voltage events from a zonalperspective. Again, all of this may be automated using the techniquesdescribed above for establishing zones, prioritizing based on thehistorical effects of voltage events within the electrical system, andproviding the energy consumer with analyses summaries based on theseclassifications.

Zones are also useful for identifying practical and economicalapproaches to mitigate voltage events (or other PQ issues). Becausemitigation solutions can range from system-wide to targeted schemes, itis beneficial to evaluate mitigation opportunities the same way. Asshown in FIG. 21 above, for example, mitigation solutions for voltageevents become more expensive as the proposed solution moves closer tothe electrical main switchgear.

In embodiments, evaluating zones to identify mitigation opportunities ofvoltage events can produce a more balanced, economical solution. Forexample, one zone may be more susceptible to voltage events (e.g.,perhaps due to a local motor starting) than another zone. It may bepossible to provide electrical service to sensitive loads from anotherzone. Alternatively, it may be prudent to move the cause of the voltageevents (e.g., the local motor) to another service point in another zone.

A further example benefit of evaluating zones is the ability toprioritize capital expenditure (CAPEX) investments for voltage eventmitigation based on the prioritization of each respective zone. Assumingthe zones have been properly prioritized/ranked, important metrics suchas percent load impacted (relative), total load impacted (absolute),worst case severity, recovery time, etc. may be aggregated over time toindicate the best solution and location for mitigative equipment. Usingaggregated zonal voltage tolerance data from IEDs within the zone mayprovide a “best-fit” solution for the entire zone or locate a targetedsolution for one or more loads within a zone.

IV. Alarm Management of IEDs Using Dynamic Tolerance Curves andAssociated Impact Data

As discussed above, each location within an electrical system/networkgenerally has unique voltage event tolerance characteristics.Dynamically (continuously) generating the distinct voltage eventtolerance characteristics for one or more metered points in theelectrical system provides many benefits including a betterunderstanding of an electrical system's behavior at the metered point,suitable and economical techniques for mitigating voltage anomalies,verification that installed mitigation equipment meets its designcriteria, leveraging non-PQ IEDs to help characterize voltage eventtolerances, and so forth.

Another example advantage of characterizing a IED point's voltage eventtolerance is to customize alarms at the IED's point of installation.Using dynamic voltage event characterization to manage alarms providesseveral benefits including ensuring 1) relevant events are captured, 2)excessive alarms are prevented (better “alarm validity”), 3) appropriatealarms are configured, and 4) important alarms are prioritized.

Existing approaches to alarm configuration and management often include:

-   -   Manual configuration by energy consumer based on standards,        recommendations, or guessing.    -   Some form of setpoint learning that necessitated a configuration        “learning period” to determine what was normal. Unfortunately,        if an event occurred during the learning period, it would be        considered normal behavior unless the energy consumer caught it        and omitted the data point.    -   “Capture Everything” approach that requires the energy consumer        to apply filters to distinguish which alarms are important and        which are not.

In short, the energy consumer (who may not be an expert) could berequired to actively discriminate which event alarms/thresholds areimportant, either before or after the event alarms are captured in a“live system.”

Currently, IED voltage event alarms have two important thresholds thatare typically configured: 1) magnitude, and 2) duration (sometimesreferred to as alarm hysteresis). Equipment/loads are designed tooperate at a given optimal voltage magnitude (i.e., rated voltage)bounded by an acceptable range of voltage magnitudes. Additionally, itmay be possible for a load to operate outside the acceptable voltagerange, but only for short periods of time (i.e., duration).

For example, a power supply may have a rated voltage magnitude of 120volts rms±10% (i.e., ±12 volts rms). Therefore, the power supplymanufacturer is specifying the power supply should not be operatedcontinuously outside the range of 108-132 volts rms. More precisely, themanufacturer is making no promises regarding the power supply'sperformance or susceptibility to damage outside their prescribed voltagerange. Less evident is how the power supply performs during momentary(or longer) voltage excursions/events outside the prescribed voltagerange. Power supplies may provide some voltage ride-though due to theirinherent ability to store energy. The length of voltage ride-throughdepends on a number of factors, primarily the amount/quantity of loadconnected to the power supply during the voltage excursion/event. Thegreater the load on the power supply, the shorter the power supply'sability to ride-though the voltage excursion/event. In summary, thissubstantiates the two parameters (voltage magnitude and duration duringthe voltage event), which also happen to be the same two parametersexemplified in basic voltage tolerance curves. It further validates loadas an additional parameter that may be considered where a voltageevent's impact and IED alarm thresholds are concerned.

In embodiments of this disclosure, a IED device's voltage magnitudealarm threshold may be initially configured with a reasonable setpointvalue (e.g., the load's rated voltage±5%). The corresponding durationthreshold may be initially configured to zero seconds (highest durationsensitivity). Alternatively, the IED device's voltage magnitude alarmthreshold may be configured for ANY voltage excursion above or below theload's rated voltage (highest magnitude sensitivity). Again, thecorresponding duration threshold (alarm hysteresis) may be initiallyconfigured to zero seconds (highest sensitivity).

As the metered voltage deviates beyond the voltage alarm threshold(regardless of its configured setpoint), the IED device may alarm on avoltage disturbance event. The IED may capture characteristics relatedto the voltage event such as voltage magnitude, timestamp, eventduration, relevant pre/during/post-event electrical parameters andcharacteristics, waveform and waveform characteristics, and/or any othermonitoring system indication or parameter the IED is capable ofcapturing (e.g., I/O status positions, relevant time stamps, coincidentdata from other IEDs, etc.).

Voltage events may be evaluated to determine/verify whether a meaningfuldiscrepancy exists between a pre-event electrical parameter's value(e.g., load, energy, phase imbalance, current, etc.) and itscorresponding post-event value. If a discrepancy does not exist(pre-event vs. post-event), the voltage event may be considered to be“non-impactful” meaning there is no indication the energy consumer'soperation and/or equipment was functionally affected by the voltageevent. The voltage event data may still be retained in memory; however,it may be classified as non-impactful to the energy consumer's operationat the point where the IED captured the voltage event. The existingvoltage alarm magnitude and duration threshold setpoints may thenreconfigure to the magnitude and duration of the non-impactful event(i.e., reconfigured to less sensitive setpoints). Ultimately, inembodiments the more severe voltage event that does not indicate anyoperational and/or equipment functional impact at the IED point willbecome the new voltage magnitude and duration threshold for the voltageevent alarms for that respective IED.

If a pre-event vs. post-event discrepancy does exist, the voltage eventmay be considered to be “impactful” meaning there is at least oneindication the energy consumer's operation and/or equipment wasfunctionally affected by the voltage event. The voltage event data maybe retained in memory, including all measured/calculated data andmetrics related to the impactful event (e.g., % impacted, absoluteimpact, voltage magnitude, event duration, etc.). Moreover, additionalrelevant data associated with the voltage event may be appended to thevoltage event data record/file at a later time (e.g., calculatedrecovery time from event, additional voltage event information fromother IEDs, determined event source location, metadata, IED data, otherelectrical parameters, updated historical norms, statistical analysis,etc.). Because the voltage event is determined to be “impactful,” thevoltage alarm magnitude and duration threshold setpoints are leftunchanged to ensure less severe, yet still impactful, events continue tobe captured by the IED at its respective installation point within theelectrical system.

In embodiments, the final result of this process is the discrete IEDdevice produces a custom voltage alarm template at the point ofinstallation that indicates voltage events (and their respectivecharacteristics) producing impactful events and/or differentiatesimpactful voltage events from non-impactful voltage events. As morevoltage events occur, the custom voltage alarm template more accuratelyrepresents the true voltage event sensitivity at the IED's point ofinstallation. In embodiments, it is possible to capture any (orsubstantially any) voltage event that exceeds any standardized or customthreshold; however, the energy consumers may choose to prioritizeimpactful events as a distinctive category of alarms/indicators. Thiscould be used, for example, to minimize the inundation of superfluousvoltage alarms in the energy consumer's monitoring system byannunciating only prioritized alarms considered to indicate an impactfulhad occurred.

As indicated above in connection with other embodiments of thisdisclosure, the tailored voltage tolerance curve built for customizedvoltage event alarm annunciation could also be used to recommendmitigation equipment to improve ride-through characteristics at theIED's point of installation. Should the energy consumer installmitigation equipment, a manual or automatic indication can beprovided/detected by the system so a new version of the voltagetolerance template can be created based on the system modification(e.g., mitigation equipment installation). In embodiments, a practicalapproach may be a manual indication of supplemental mitigation equipmentbeing added to the system; however, an automatic indication could alsobe provided based on “uncharacteristic changes” in the electricalsystem's response to voltage events at the point of the IED'sinstallation, for example. These “uncharacteristic changes” could beestablished, for example, by statistically evaluating (e.g., viaanalytics algorithms) one or more electrical parameters (i.e., voltage,current, impedance, load, waveform distortion, and so forth). Inembodiments, they may also be identified by any sudden change in voltageevent ride through at the point of the IED's installation. A query maybe made of the energy consumer or electrical system manager to validateany additions, eliminations or changes to the electrical network.Feedback from the energy consumer could be used to better refine anystatistical evaluations (e.g., analytics algorithms) related to voltageevents (or other metering features). Historical information (includingcustomized voltage tolerance curves) would be retained for numerousassessments such as verification of the effectiveness of mitigationtechniques, impact of new equipment installation to voltage ride-throughcharacteristics, and so forth.

As part of this embodiment, more than two event parameters may be usedto configure thresholds to trigger alarms for voltage events. In thedescription above, the magnitude of voltage deviation and the durationof the voltage event are used configure and trigger voltage eventalarms. In embodiments, it is also possible to include more dimensionssuch as load impact and/or recovery time to configure voltage eventalarms. Just as it is possible to set voltage event setpoint thresholdsto alarms only when any load is impacted, it is also possible toconfigure voltage event setpoint thresholds to allow some level ofimpact to the load. Through load identification, either manually orautomatically (based on electrical parameter recognition), it ispossible to alarm when only certain types of loads experience an impactdue to a voltage event. For example, some loads have certain signaturessuch as elevated levels of specific harmonic frequencies. Inembodiments, it would be possible to trigger a voltage event alarm ifthose specific harmonic frequencies are no longer evident.

It is possible to use other parameters to customize the alarm templates.For example, the energy consumer may only be interested in voltageevents with a recovery times greater than 5 minutes. Voltage eventcharacteristics that typically produce recovery times shorter than 5minutes could be filtered by using historical event data to configurethe alarm templates accordingly. Moreover, energy consumers may only beinterested in voltage events that generate monetary losses greater than$500. Again, voltage event characteristics that typically producemonetary losses less than $500 could be filtered using historical datato configure the alarm templates accordingly. As is apparent, any otheruseful parameter derived from voltage event characteristics may besimilarly used to tailor and provide practical alarm configurations.Multiple parameters may also be concurrently used (e.g., recoverytimes >5 minutes AND monetary losses >$500) to provide more complexalarm schemes and templates, and so forth.

In embodiments, as more voltage events occur, additional voltagepre/during/post-event attributes and parameters are captured at both thediscrete and system level and integrated into typical historicalcharacterizations (historical norms). This additional characterizationof voltage events can be used, for example, to estimate/predict theexpected recovery time from both a discrete and system level.Additionally, recommendations can be made to energy consumers on how toachieve a faster recovery time based on historical event data regardingthe effective sequencing to reenergize loads.

In embodiments, customer alarm prioritization can be performed (forvoltage events or any other event type) based on the level of loadmeasured at one or more discrete metering/IED points within theelectrical system. When some indication is received from ametered/virtual/IED point that a load or loads have changed (or areoperating atypically), voltage event alarm setpoint thresholds may bereevaluated and modified based on the level of load measured at one ormore discrete (or based on the load's atypical operation). For example,it may be advantageous to null, silence or deprioritize the voltageevent alarm when one or more IEDs indicate the measure load is low(indicating the facility is off-line). Conversely, raising the priorityof the voltage event alarm would be prudent as one or more IEDs indicateadditional loads being started.

As mentioned earlier in this section, in embodiments it is possible touse this feature to prioritize alarms (including voltage event alarms).The IED may be configured to capture data related to substantially anyperceptible voltage variation from the nominal voltage (or load(s) ratedvoltage) at the point of installation, and take an action(s) includingstoring, processing, analyzing, displaying, controlling, aggregating,and so forth. Additionally, the same action(s) may be performed onsubstantially any alarms (including voltage event alarms) that exceedsome pre-defined setpoint/threshold such as those defined by a dynamicvoltage tolerance curve, standard(s), or other recommendations (asderived from any number or combination of electrical parameters, I/O,metadata, IED characteristics, etc.). In embodiments, any or allcaptured events (including voltage events) may then be analyzed toautomatically prioritize the alarms at a discrete, zone and/or systemlevel based on any number of parameters including: alarm type, alarmdescription, alarm time, alarm magnitude, affected phase(s), alarmduration, recovery time, waveform characteristics, load impactassociated with an alarm, location, hierarchical aspects, metadata, IEDcharacteristics, load type, customer type, economic aspects, relativeimportance to operation or load, and/or any other variable, parameter orcombination thereof related to the event (including voltage events) andthe energy consumer's operation. Prioritizing may be relevant for theinherent characteristics of discrete events or involve comparisons ofmore than one event (including voltage events), and may be performed asevents originate, deferred to a later time, or dependent on theaforementioned parameters. In embodiments, prioritization may beinteractive with the energy consumer, automated, or both with the goalbeing to facilitate the energy consumer's preferences.

In embodiments, parameters to be considered may include at leastelectrical data (from at least one phase), control data, time data,metadata, IED data, operational data, customer data, load data,configuration and installation data, energy consumer preferences,historical data, statistical and analytical data, economic data,material data, any derived/developed data, and so forth.

For example, FIG. 34 illustrates a relatively simple voltage tolerancecurve for an IED with voltage alarm thresholds set at ±10% of thenominal voltage for events arbitrarily ranging from 1 usec tosteady-state. In FIG. 35, a voltage sag event occurs on this IED thatsags to 50% of the nominal voltage and lasts for 3 milliseconds induration. Pre/during/post-event analysis of this event indicates no loadwas impacted. In embodiments, because no load was impacted, the alarmsetpoint thresholds in the IED are reconfigured to indicate/prioritizethe occurrence of a voltage event when (sometimes, only when) themagnitude and duration of a voltage event are more severe than the eventdescribed in FIG. 35. FIG. 36 illustrates changes made to the originalvoltage-tolerance curve. In short, voltage events occurring in the redarea of the graph are expected to be non-impactful and voltage eventsoccurring in the green area of the graph may or may not be impactful. InFIG. 37, another voltage event occurs and is captured by the same IED.In this second voltage event, a voltage interruption (to 0% of thenominal voltage) occurs and lasts for 1 millisecond in duration. Again,pre/during/post-event analysis of the second event indicates no load wasimpacted. And again, the alarm setpoint thresholds in the IED arereconfigured to indicate/prioritize the occurrence of a voltage eventwhen (sometimes, only when) the magnitude and duration of the voltageevent are more severe than the event described in FIG. 36. FIG. 38illustrates changes made to the original voltage-tolerance curve.

In FIG. 39, a third voltage event occurs and is captured by the IED. Inthis third voltage event, the voltage sags to 30% of the nominal voltageand lasts for 2 milliseconds in duration. This time thepre/during/post-event analysis of the third event indicates 25% of theload was impacted (e.g., disconnected) at 30% of the nominal voltage.Subsequently, the alarms setpoint thresholds are left unchanged becauseof the 25% impact to the load (i.e., a load impact occurred where it wasexpected to occur). FIG. 40 illustrates the final settings of thevoltage event alarm threshold after these three voltage events. Notethat the third event is not shown on the graph because the purpose ofthis embodiment of the disclosure is to reconfigure/modify voltage eventsetpoint thresholds. The energy consumer may be notified of the thirdevent occurrence, and the voltage event data, calculations, derivationand any analyses may be stored for future reference/benefits.

V. Evaluating and Quantifying Voltage Event Impact on Energy and Demand

Establishing the losses incurred due to voltage events is oftencomplicated; however, embodiments of this disclosure provide aninteresting metric (or metrics) to help quantify the energy and demandcontribution to the total losses. When a voltage event occurs, facilityprocesses and/or equipment may trip off-line. The activity of restartingprocesses and/or equipment consumes energy and can (in some cases)produce a peak demand for the facility. Although these costs arefrequently overlooked, they may be considerable over time whilecontributing little to the actual production and profitability of afacility's operation. There may be ways to recoup some of these coststhrough insurance policy coverage, tax write-offs in some jurisdictions,and even peak demand “forgiveness” from the utility. Perhaps mostimportantly, quantifying the financial impact of voltage events toutility bills can provide incentives to mitigate the voltage eventsleading to these unexpected and potentially impactful losses.

When a voltage event occurs, the analyses described above may beperformed to determine the level of impact to the load or operation. Ifno evidence is found of an impact on a load, process, and/or system,this aspect of this embodiment of the disclosure may be disregarded. Ifthe voltage event is found to have impacted a load, process, and/orsystem, the pre/during/post-event analyses of electrical parameters areperformed. The recovery time clock starts and this embodiment of thedisclosure categorizes the energy consumption, demand, power factor, andany other parameter related to the utility billing structure asassociated with the recovery time interval. Evaluation and analyses maybe performed on these parameters to determine discrete, zonal and/orsystem metrics (including aggregation), comparisons to historical eventmetrics, incremental energy/demand/power factor costs and so forth.These metrics may be evaluated against local utility rate structures tocalculate the total energy-related costs for recovery, discrete, zonal,and/or systems most susceptible and most costly during the recoveryperiod for targeted mitigation, expectations based on historical voltageevent data (e.g., number of events, recovery period of events, energycosts for events, etc.), opportunities to operationally/procedurallyimprove voltage event response time, and so forth.

In embodiments, the data and analyses collected before, during and/orafter the recovery period may be filtered, truncated, summarized, etc.to help the energy consumer better understand the impact of the voltageevent (or other event) on their electrical system, processes, operation,response time, procedures, costs, equipment, productivity or any otherrelevant aspect of their business's operation. It can also provide auseful summary (or detailed report) for discussions with utilities,management, engineering, maintenance, accounting/budgeting, or any otherinterested party.

VI. Disaggregation of Typical and Atypical Operational Data UsingRecovery Time

It is important to recognize a facility's operation during a recoveryperiod is often aberrant or atypical as compared to non-recovery times(i.e., normal operation). It is useful to identify, “tag” (i.e.,denote), and/or differentiate aberrant or atypical operational data fromnormal operational data (i.e., non-recovery data) for performingcalculations, metrics, analytics, statistical evaluations, and so forth.Metering/monitoring systems do not inherently differentiate aberrantoperational data from normal operational data. Differentiating andtagging operational data as either aberrant (i.e., due to being inrecovery mode) or normal provides several advantages including, but notlimited to:

-   1. Analyses (such as the aforementioned) may assume operational    uniformity throughout all the data; however, it is useful to    disaggregate aberrant or atypical operational patterns from normal    operational patterns to better evaluate and understand the    significance of the data being analyzed. Data analysis is improved    by providing two different categories of operations; normal and    aberrant/abnormal/atypical. Each may be analyzed automatically and    independently to provide unique and/or more precise information    regarding each operational mode within a facility or system.    Differentiating normal operational data from atypical operational    data (i.e., due to a voltage event) further bolsters decisions made    based on the conclusions of analyses.-   2. Differentiating normal and aberrant operational modes makes it    possible to provide discrete baseline information for each    operational mode. This provides the ability to better normalize    operation data because atypical data can be excluded from analysis    of system data. Additionally, aberrant operational modes may be    analyzed to help understand, quantify and ultimately mitigate    impacts associated with impactful voltage events. In the case of    event mitigation, data analysis of aberrant operational periods will    help identify possible more effective and/or economical approaches    to reducing the impact of voltage events.-   3. Losses incurred due to voltage events are generally difficult to    establish; however, evaluations of data tagged (i.e., partitioned,    denoted, etc.) as abnormal/aberrant/atypical may be used to identify    energy consumption outliers associated with voltage events. This    information may be used to help quantify the energy and demand    contribution of events to the total losses. When a voltage event    occurs, equipment may unintentionally trip off-line. The process of    restarting equipment and processes consumes energy and can (in some    cases) produce a new peak demand for the facility. Although these    costs are frequently overlooked/missed, they may be considerable    over time while contributing little to the actual production and    profitability of the operation. There may be ways to recoup some of    these costs through insurance policy coverage, tax write-offs in    some jurisdictions, and even peak demand “forgiveness” from the    utility. Perhaps most importantly, quantifying the financial impact    of voltage events to utility bills can provide incentive to mitigate    the voltage events leading to these unexpected and potentially    impactful losses.

VII. Other Evaluations and Metrics Related to Voltage Event Impact andRecovery Time

As is known, voltage events including outages are a leading global causeof business interruption-related losses. The annual estimated economicloss for medium and large businesses is estimated to be between $104billion and $164 billion based on a study by Allianz Global. Inembodiments, by incorporating additional economic metadata, it ispossible to evaluate individual voltage events to determine the monetaryimpact of these events. Additionally, in embodiments it is possible tototalize the voltage event impacts by aggregating data and informationfrom individual events. Some example useful financial information tohelp quantify the economic impact of voltage events include: averagematerial loss/event/hour, utility rate tariffs (as discussed above),average production loss cost/event/hour, estimated equipmentloss/event/hour, average 3^(rd) party costs/event/hour, or any othermonetary metric related to the cost of downtime on a per event ordaily/hourly/minutely basis. Using the recovery time from thecalculations described above, metrics may be determined forsubstantially any loss that has been monetarily quantified. Thesemetrics may be determined at a discrete IED, zone and/or system levelaccordingly.

A number of new voltage event-related indices are set forth herein asuseful metrics for qualifying and quantifying voltage events andanomalies. While these new indices focus on voltage sags, in embodimentsthey may also be considered for any other voltage event or category ofpower quality event. Example indices include:

-   -   Mean Time Between Events (MTBE). As used herein, the term “MTBE”        is used to describe the average or expected time a system or        portion of a system is operational between events and their        subsequent recovery time. This includes both impactful and        non-impactful events, so there may or may not be a quantity of        recovery time associated with each event.    -   Mean Time Between Impactful Events (MTBIE). As used herein, the        term “MTBIE” is used to describe the average or expected time a        system or portion of a system is operational between events and        their subsequent recovery time. In embodiment, this metric is        limited to only impactful events and will likely have some        quantity of recovery time associated with each event.    -   Mean Time to Restart (MTTR). As used herein, the term “MTTR” is        used to describe the average time it takes to restart production        at a system or portion of a system (e.g., load, zone, etc.)        level. This “average time” includes all (or substantially all)        factors involved in restarting production including (but not        limited to): repairs, reconfigurations, resets,        reinitializations, reviews, retests, recalibrations, restarts,        replacing, retraining, relocating, revalidations, and any other        aspect/function/work effecting the recovery time of an        operation.    -   Sag rate. As used herein, the term “sag rate” is used to        describe the average number of voltage sag-events of a system or        portion of a system over a given time period such as hours,        months, years (or other time period).    -   Production Availability. As used herein, the term “production        availability” generally refers to the immediate readiness for        production, and is defined as the ability of a facility to        perform its required operation at a given time or period. This        metric focuses on event-driven parameter(s) and may be        determined by:

${PA}_{i} = \frac{MTBIE}{{MTBIE} + {MTTR}}$

In embodiments, systems, zones, and/or discrete IED points may becharacterized by their “Number of 9's Production Up-Time,” which is anindication of the production availability exclusive of the recovery timeduration. Similar to the number of 9's in the usual connotation, thismetric may be determined annually (or normalized to an annual value) toprovide an indication or metric of the impact of voltage events (orother events) on an operation's productivity. This metric may be usefulto help identify mitigation investment opportunities and to prioritizethose opportunities accordingly.

In embodiments, it is possible to use the metrics set forth above toestimate/predict recovery time based on historical recovery timeinformation. A voltage event's magnitude, duration, location, metadata,IED characterization, or other calculated/derived data and information,for example, may be used to facilitate these estimations andpredictions. This measure may be performed and provided to energyconsumers at the discrete IED point, zone, and/or system level as one ormore reports, texts, emails, audible indications, screens/displays, orthrough any other interactive means.

A few examples of supplementary metrics that may be unique to an energyconsumer's operation and assist in prioritizing mitigation equipmentconsiderations for placement, investment, etc. include:

-   -   Average Zonal Interruption Frequency Index (AZIFI). AZIFI is an        example metric that can be used to quantify zones experiencing        “the most” interruptions in an electrical system. As used        herein, AZIFI is defined as:

${AZIFI} = \frac{{number}\mspace{14mu}{of}\mspace{14mu}{zone}\mspace{14mu}{impacts}\mspace{14mu}{within}\mspace{14mu}{facility}}{{total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{zones}\mspace{14mu}{in}\mspace{14mu}{the}\mspace{14mu}{system}}$

-   -   Zonal Impact Average Interruption Frequency Index (ZIAIFI).        ZIAIFI is an example metric that can be used to show trends in        zone interruptions along with number of zones affected in        electrical system. As used herein, ZIAIFI is defined as:

${ZIAIFI} = \frac{{number}\mspace{14mu}{of}\mspace{14mu}{zone}\mspace{14mu}{impacts}}{{number}\mspace{14mu}{of}\mspace{14mu}{zones}\mspace{14mu}{that}\mspace{14mu}{had}\mspace{14mu}{at}\mspace{14mu}{least}\mspace{14mu}{one}\mspace{14mu}{impact}}$

-   -   Average Zonal Interruption Duration Index (AZIDI). AZIDI is an        example metric that can be used to indicate an overall        reliability of the system based on an average of zone impacts.        As used herein, AZIDI is defined as:

${AZIDI} = \frac{{sum}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{recovery}\mspace{14mu}{time}\mspace{14mu}{durations}\mspace{14mu}{of}\mspace{14mu}{all}\mspace{14mu}{impacted}\mspace{14mu}{zones}}{{total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{zones}\mspace{14mu}{in}\mspace{14mu}{the}\mspace{14mu}{system}}$

-   -   Zonal Total Average Interruption Duration Index (ZTAIDI). ZTAIDI        is an example metric that can be used to provide an indication        of the average recovery period for zones that experienced at        least one impactful voltage event. As used herein, ZTAIDI is        defined as:

${ZTAIDI} = \frac{{sum}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{durations}\mspace{14mu}{of}\mspace{14mu}{all}\mspace{14mu}{zone}\mspace{14mu}{impacts}}{{number}\mspace{14mu}{of}\mspace{14mu}{zones}\mspace{14mu}{that}\mspace{14mu}{experienced}\mspace{14mu}{at}\mspace{14mu}{least}\mspace{14mu}{one}\mspace{14mu}{impact}}$

While the foregoing metrics focus on zone-related impacts, inembodiments some or all concepts may be reused for discrete IED pointsor (in some cases) system impact metrics. It is understood that thepurpose here is to document examples of the ability to create usefulmetrics for energy consumers and their operations; not to define everypossible metric or combination thereof.

It is also understood that each of the metrics discussed above may befurther determined and partitioned for upstream, downstream, internal(e.g., facility), and external (e.g., utility) voltage event sources asappropriate. The latter two mentioned (internal/external) may requiresome level of hierarchical classification of the IED and/or electricalsystem. Other classifications of hierarchies (e.g., protection layoutschemes, separately derived sources, processes or sub-systems, loadtypes, sub-billing groups or tenants, network communications schemes,etc.) may be used to create/derive additional useful metrics as neededto better evaluate the impact of voltage events on a facility'soperation, for example. Outputs from embodiments of this disclosure maybe provided by one or more reports, texts, emails, audible indications,screens/displays, or through any other interactive means. Indicationsmay be provided at the IED, on-site software, cloud, gateway, or othermonitoring system component and/or accessory. In embodiments, theoutputs and indications may be generated by circuitry and systemsaccording to the disclosure in response to the circuitry and systemsreceiving and processing respective inputs.

VIII. Voltage Event Recovery Status Tracking

An example method according to the disclosure for reducing recovery timeperiods includes providing a method of tracking the recovery as itprogresses. By identifying and monitoring the recovery periods throughdiscrete IEDs, zones, hierarchies, and/or the system in real-time, theenergy consumer (and the systems and methods disclosure herein) is/arebetter able to identify, manage, and expedite the recovery process foran event throughout their facility. Event recovery tracking allowsenergy consumers to understand the status of the recovery and makebetter and quicker decisions to facilitate its recovery. This embodimentwould also allow the energy consumer to review historical data to makerecovery improvements, produce and/or update recovery procedures,identify zonal recovery constrictions, troublesome equipment, and soforth to improve future event recovery situations (and thus, increasesystem uptime and availability). Alarming capabilities may beincorporated into recovery situations to provide indications ofconstraining locations within zones or the facility. Historical recoverymetrics or some other configured setpoints may be used to determinerecovery alarm threshold settings for IEDs, system software, and/orcloud, and outputs from embodiments of this disclosure may be providedby one or more reports, texts, emails, audible indications,screens/displays, or through any other interactive means.

IX. Developing Various Baselines Related to Voltage Events

Another example method for determining expected recovery times usesfactors such as market segments and/or customer types, processes-basedevaluations, and/or load and equipment types to determine the expectedrecovery times. By defining recovery times based on these and otherfactors, for example, a recovery time baseline or reference can bedeveloped with respect to a voltage event's magnitude, duration, percentload impacted, and/or any other electrical parameter, metadata, or IEDspecification. The baselines/references may be used to set recoveryalarm thresholds, assess recovery time performance and identifyopportunities for improvement, estimate actual vs. expected recoverytime and costs, improve accuracy of estimates for impactful voltageevents, and so forth. Actual historical voltage event impact andrecovery time data may be used to produce relevant models throughvarious means including statistical analyses (and/or analytics) andevaluations, simple interpolation/extrapolation, and/or any other methodthat produces a reasonable typical value(s). Baseline/reference modelsmay range from simple to complex, and may be created or determined fordiscrete IED locations, zones, or entire systems, and outputs fromembodiments of this disclosure may be provided by one or more reports,texts, emails, audible indications, screens/displays, or through anyother interactive means.

X. Evaluating Voltage Event for Similarities to Identify RepetitiveBehavior

In embodiments, evaluating voltage events across an electrical system toexamine event similarity may be useful for energy consumers.Similarities may be in event time of occurrence, seasonality, recoverytime characteristics, behavior of electrical parameters, behavior ofzonal characteristics, behavior of operational processes, and/or anyother notable behaviors or commonalities. Identifying repetitivebehaviors and/or commonalities may be an important tactic forprioritizing and resolving voltage event effects. Moreover,analysis/analytics of historical data may provide the ability to predictthe system impact and recovery time due to a voltage event after theinitial onset of said voltage event.

XI. Voltage Event Forecasting

As mentioned in previous embodiments of the disclosure, it is importantto be able to identify beneficial opportunities for energy consumers tomitigate voltage events. Another metric that may be considered isforecasting an estimated number of interruptions, estimated impact, andtotal recovery time (and associated costs). In embodiments, this metricmay be extremely useful for planning purposes, support of capitalinvestment opportunities in voltage event mitigation equipment, and evento forecast expected savings for installing said mitigation equipment.These forecasts may be evaluated at a later point in time to ascertaintheir accuracy and to fine-tune forecasts and expectations goingforward.

XII. Other Graphs and Diagrams Related to Voltage Event Impact andRecovery Time

Aside from the various plots (or graphs) discussed in connection withthe embodiments described above, there are other additional usefulmethods to display data related to voltage events. The graphs describedbelow in connection with FIGS. 41-44, for example, are only a fewexamples of displaying data in a useful format; there may be many othermethods to present voltage event data in a meaningful way that canbenefit energy consumers. Graphs, charts, tables, diagrams, and/or otherillustrative techniques, for example, may be used to summarize, compare,contrast, validate, order, trend, demonstrate relationships, explain,and so forth. These data types may be real-time, historical, modeled,projected, baseline, measured, calculated, statistical, derived,summarized, and/or estimated. Graphs may also be any dimension (e.g.,2-D, 3-D, etc.), color, shade, shape (e.g., line, bar, etc.), etc. toprovide a unique and useful perspective.

FIG. 41 illustrates an example of the load impact versus recovery timefor a single event. The green area is indicative of normal or expectedrange of operational parameters, the shaded orange area is highlightingthe recovery time period, and the black line is the load as a functionof time. FIG. 42 illustrates an example of a series of impactful eventsversus their recovery time from a single IED (multiple IEDs could alsobe used here). In this example, the green area is indicative of normalor expected operational parameters, and the shaded orange highlights theperiods when the system has experienced an impactful event andexperienced a recovery period. FIG. 43 illustrates an example ofadditional data being integrated with the data shown in FIG. 41. In thisexample, the green area is indicative of normal or expected range ofoperational parameters, the shaded orange is highlighting the recoverytime period, the black line is showing the load as a function of time,the dashed pink line is showing the expected load as a function of time,and the dashed blue line shows a typical pre-event profile. As a rule ofthumb, the behavior of upstream events may be more unpredictable thandownstream events over time. FIG. 44 illustrates an example ofpre/during/post-event percent of load impact versus recovery time for avoltage event. Again, different variables, metrics parameters,characteristics, etc. may be graphed, illustrated, etc. shown as neededor useful.

XIII. Aggregation/consolidation of Voltage Event Impact and RecoveryTime Data

As is known, voltage events are often extensive, impacting multipleloads, processes, and even the entire system concurrently. Inembodiments, metering systems according to the disclosure may exhibitmultiple alarms from different IEDs located across the facility. Sourceevents generally impact the entire system, for example, resulting inevery (or substantially every) capable IEDs indicating an event hasoccurred.

In embodiments, aggregating/consolidating the multitude of voltage eventdata, alarms and impacts across a system is important for severalreasons. First, many energy consumers have a tendency to ignore “alarmavalanches” from monitoring systems, so aggregating/consolidatingvoltage event data decreases the number alarms the energy consumer hasto review and acknowledge. Second, the data from a flurry of alarms isoften the result of one voltage event coming from the same root cause.In this case, it is much more efficient to reconcile all coincidentvoltage events captured by multiple IEDs into a single event forreconciliation. Third, bundled voltage events are much easier to analyzethan independent voltage events as most of the relevant data andinformation is available in one place. For the sake of brevity, thereare many other reasons to aggregate/consolidate voltage events notlisted here.

The ability to aggregate/consolidate the impact of voltage events andtheir often-accompanying recovery times is important because it helpsavoid redundancy of event data. Redundant event data can skew metricsand exaggerate conclusions, which may results in flawed decisions. Thisdisclosure focuses on three layers of aggregation/consolidation withinelectrical systems: IED, zonal and system.

In embodiments, the first layer (IED) requires minimalaggregation/consolidation because data is acquired from a singlepoint/device and (hopefully) the device shouldn't be producing redundantinformation within itself from voltage events. In some cases, there maybe somewhat superfluous alarm information from a single device. Forexample, a three-phase voltage event may provide one alarm for each ofthe three phases experiencing the voltage event. Moreover, an alarm maybe triggered for both the event pickup and dropout, resulting in sixtotal voltage event alarms (a pickup and dropout alarm for each of thethree phases). While this example of alarm abundance may be bothersomeand confusing, many devices and monitoring systems alreadyaggregate/consolidate multiple event alarms as just described into asingle event alarm. In some embodiments, a single voltage event alarmmay be provided from each IED for each voltage event that occurs in theelectrical system.

It was mentioned above that a voltage event often impacts multiple IEDswithin a monitoring system; specifically, those that are capable ofcapturing anomalous voltage conditions. Since zones and systemstypically consist of multiple IEDs, the need to aggregate/consolidatethe impact and subsequent repercussions of voltage events lies withthese two (zones and systems). Although a zone may encompass an entiresystem, zones are configured as a subset/sub-system of the electricaland/or metering system. However, because zones and systems bothgenerally consist of multiple devices, they will be treated similarly.

In embodiments, there are different methods/techniques toaggregate/consolidate zones. A first example method includes evaluatingthe voltage event impact and recovery time from all IEDs within aparticular zone and attributing the most severe impact and recovery timefrom any single IED within that zone to the entire zone. Because theevent impact and recovery time are independent variables, and thereforemay be derived from different IEDs, these two variables should betreated independently from each other. Of course, it would be importantto track which zonal device was considered/recognized as havingexperienced the most severe impact and which zonal device experiencedthe longest recovery time. This same approach could be used for systemsby leveraging the conclusions generated from the zone evaluations.Ultimately, the recovery time for a system is not completed until allrelevant IEDs indicate that is the case.

A second example method includes assessing a voltage event within a zoneby using statistical evaluations (e.g., average, impact and averagerecovery time, etc.) from all IEDs with a particular zone. In this case,the severity of a voltage event may be determined for the entire zone bystatistically appraising data from all IEDs and providing results torepresent the entire zone for each particular voltage event. Statisticaldeterminations including means, standard deviations, correlations,confidence, error, accuracy, precision, bias, coefficients of variation,and any other statistical methods and/or techniques may be employed toaggregate/consolidate the data from multiple IEDs to a representativevalue or values for the zone. The same statistical approach may be usedto combine zones into representative metrics/values for system impactand recovery time. Again, the recovery time for a system will becontingent on each relevant IED indicating that is the case.

Another example method to evaluate voltage events is by load-type. Inembodiments, the energy consumer (or systems and method disclosedherein) may choose to categorize and aggregate/consolidate loads bysimilarity (e.g., motors, lighting, etc.) regardless of their locationwithin the facility's electrical system, and evaluate the impact andrecovery time of those loads accordingly. It would also be possible toevaluate voltage events by their respective processes. Byaggregating/consolidating loads (regardless of type, location, etc.)associated with the same process, the impact and recovery time could bequantified for said process. Another method to aggregate/consolidatevoltage events is by sources and/or separately derived sources. Thisapproach would help quantify the impact and recovery time of a voltageevent as it related to the energy source within the facility (or out onthe utility network). Other useful and logical methods toaggregate/consolidate voltage event information from two or more IEDsmay be considered as well (e.g., by building, by product, by cost, bymaintenance, and so forth).

In embodiments, a fundamental purpose of aggregating/consolidatingvoltage event data is to identify opportunities to decrease theseevents' overall impact on the energy consumer's business to reducedowntime and make it more profitable. One or more of the methods (orcombinations of methods) described herein may be used to meet thisobjective. It may be useful or even required to have one or more ofthese methods configured by the energy consumer (or surrogate), or thesystem and methods disclosed herein. The ability to consider the voltageevent impact and recovery time at discrete IEDs is not mutuallyexclusive from any approach to consider and evaluateaggregated/consolidated voltage event impact and recovery time.

Another interesting prospect would be evaluating the performance of theenergy consumer's operation after the initial voltage event occurs. Forexample, a voltage event may result in one load tripping off-line.Shortly after, another related load may also trip off-line as a resultof the first load tripping; not due to another voltage event. The extentof this chain reaction/propagation would be of interest when determiningconsequences of providing ride-through mitigation for the first load. Inthis example, providing a timeline of load reactions over the recoveryperiod due to the original voltage event may be prudent to help minimizethe overall impact of voltage events on the energy consumer's operation.

In embodiments, outcomes from analyses of the voltage and current dataapply to the point in the network where the IED capturing the data isconnected. Each IED in the network may typically yield distinct analysesof the event, assuming each IED is uniquely placed. As used herein, theterm “uniquely placed” generally refers to the location of theinstallation within the electrical system, which impacts impedance,metered/connected loads, voltage levels, and so forth. In some cases, itmay be possible to interpolate or extrapolate voltage event data on acase-by-case basis.

In embodiments, in order to accurately characterize power quality events(e.g., voltage sags) and their subsequent network impact(s), it isimportant to measure the voltage and current signals associated with theevent. Voltage signals can be used to characterize the event, currentsignals can be used to quantify the event's impact, and both voltage andcurrent can be used to derive other relevant electrical parametersrelated to this disclosure. Although outcomes from analyses of thevoltage and current data apply to the point in the network where the IEDcapturing the data is connected, it may be possible to interpolateand/or extrapolate voltage event data on a case-by-case basis. Each IEDin the network typically yields distinct analyses of the event, assumingeach IED is uniquely placed.

In embodiments, there are multiple factors that can influence the impact(or non-impact) of a voltage sag. The impedance of the energy consumer'selectrical system may cause voltage events to produce more severevoltage sags deeper into the system hierarchy (assuming a radial-fedtopology). Voltage event magnitudes, durations, fault types, operationalparameters, event timing, phase angles, load types, and a variety ofother factors related to functional, electrical, and even maintenanceparameters can influence the effects of voltage sag events.

It is understood that any relevant information and/or data derived fromIEDs, customer types, market segment types, load types, IEDcapabilities, and any other metadata may be stored, analyzed, displayed,and/or processed in the cloud, on-site (software and/or gateways), or ina IED in some embodiments.

Referring to FIGS. 45-48, several flowcharts (or flow diagrams) areshown to illustrate various methods of the disclosure. Rectangularelements (typified by element 4505 in FIG. 45), as may be referred toherein as “processing blocks,” may represent computer software and/orIED algorithm instructions or groups of instructions. Diamond shapedelements (typified by element 4525 in FIG. 45), as may be referred toherein as “decision blocks,” represent computer software and/or IEDalgorithm instructions, or groups of instructions, which affect theexecution of the computer software and/or IED algorithm instructionsrepresented by the processing blocks. The processing blocks and decisionblocks can represent steps performed by functionally equivalent circuitssuch as a digital signal processor circuit or an application specificintegrated circuit (ASIC).

The flowcharts do not depict the syntax of any particular programminglanguage. Rather, the flowcharts illustrate the functional informationone of ordinary skill in the art requires to fabricate circuits or togenerate computer software to perform the processing required of theparticular apparatus. It should be noted that many routine programelements, such as initialization of loops and variables and the use oftemporary variables are not shown. It will be appreciated by those ofordinary skill in the art that unless otherwise indicated herein, theparticular sequence of blocks described is illustrative only and can bevaried. Thus, unless otherwise stated, the blocks described below areunordered; meaning that, when possible, the blocks can be performed inany convenient or desirable order including that sequential blocks canbe performed simultaneously and vice versa. It will also be understoodthat various features from the flowcharts described below may becombined in some embodiments. Thus, unless otherwise stated, featuresfrom one of the flowcharts described below may be combined with featuresof other ones of the flowcharts described below, for example, to capturethe various advantages and aspects of systems and methods associatedwith dynamic tolerance curves sought to be protected by this disclosure.

Referring to FIG. 45, a flowchart illustrates an example method 4500 formanaging power quality events (or disturbances) in an electrical systemthat can be implemented, for example, on a processor of an IED (e.g.,121, shown in FIG. 1A) and/or a processor of a control system associatedwith the electrical system. Method 4500 may also be implemented remotefrom the IED and/or control system in a gateway, cloud, on-sitesoftware, etc.

As illustrated in FIG. 45, the method 4500 begins at block 4505, wherevoltage and/or current signals (or waveforms) associated with one ormore loads (e.g., 111, shown in FIG. 1A) in an electrical system aremeasured and data is captured, collected, stored, etc. by an IED (and/orcontrol system) coupled to the loads.

At block 4510, electrical measurement data from the voltage and/orcurrent signals is processed to identify at least one power qualityevent associated with one or more of the loads. In some embodiments,identifying the at least one power quality event may includeidentifying: (a) a power quality event type of the at least one powerquality event, (b) a magnitude of the at least one power quality event,(c) a duration of the at least one power quality event, and/or (d) alocation of the at least one power quality event in the electricalsystem, for example. In embodiments, the power quality event type mayinclude one of a voltage sag, a voltage swell, and a voltage transient.

At block 4515, an impact of the at least one identified power qualityevent on one or more of the loads is determined. In some embodiments,determining the impact of the at least one identified power qualityevent includes measuring one or more first parameters (e.g., “pre-event”parameters) associated with the loads at a first time (e.g., a timeprior to the event), measuring one or more second parameters (e.g.,“post-event” parameters) associated with the loads at a second time(e.g., a time after the event), and comparing the first parameters tothe second parameters to determine the impact of the at least oneidentified power quality event on the loads. In embodiments, the powerquality event(s) may be characterized as an impactful event or anon-impactful event based, at least in part, on the determined impact ofthe event(s). An impactful event may, for example, correspond to anevent that interrupts operation (or effectiveness) of the loads and/orthe electrical system including the loads. This, in turn, may impact anoutput of the system, for example, the production, quality, rate, etc.of a product generated by the system. In some embodiments, the productmay be a physical/tangible object (e.g., a widget). Additionally, insome embodiments the product may be a non-physical object (e.g., data orinformation). A non-impactful event, by contrast, may correspond to anevent that does not interrupt (or minimally interrupts) operation (oreffectiveness) of the loads and/or the electrical system including theloads.

At block 4520, the at least one identified power quality event and thedetermined impact of the at least one identified power quality event areused to generate or update an existing tolerance curve associated withthe one or more of the loads. In embodiments, the tolerance curvecharacterizes a tolerance level of the loads to certain power qualityevents. For example, the tolerance curve (e.g., as shown in FIG. 4) maybe generated to indicate a “prohibited region”, a “no damage region” anda “no interruption in function region” associated with the loads (and/orelectrical system), with the respective regions corresponds to varioustolerance levels of the loads to certain power quality events. Thetolerance curve may be displayed on a graphical user interface (GUI)(e.g., 230, shown in FIG. 1B) of the IED and/or or GUI of the controlsystem, for example. In embodiments where a tolerance curve has alreadybeen generated prior to block 4520, for example, due to there being anexisting tolerance curve, the existing tolerance curve may be updated toinclude information derived from the at least one identified powerquality event and the determined impact of the at least one identifiedpower quality event. An existing tolerance curve may exist, for example,in embodiments in which a baseline tolerance curve exists or inembodiments in which a tolerance curve has already been generated usingmethod 4500 (e.g., an initial tolerance curve generated in response to afirst or initial power quality event). In other words, in embodiments anew tolerance curve is typically not generated after each identifiedpower quality event, but rather each identified power quality event mayresult in updates being made to an existing tolerance curve.

At block 4525, which is optional in some embodiments, it is determinedif the impact of the at least one identified power quality event exceedsa threshold or falls outside of a range or region (e.g., “nointerruption in function region”) indicated in the tolerance curve. Ifit is determined that the impact of the at least one identified powerquality event falls outside of the range indicated in the tolerancecurve (e.g., the event results in an interruption to the function of aload as measured by an electrical parameter or indicated by someexternal input), the method may proceed to block 4530. Alternatively, ifis determined that the impact of the at least one identified powerquality event does not fall outside of a range indicated in thetolerance curve (e.g., the event does not result in an interruption in afunction of a load), the method may end in some embodiments. In otherembodiments, the method may return to block 4505 and repeat again. Forexample, in embodiments in which it is desirable to continuously (orsemi-continuously) capture voltage and/or current signals and todynamically update the tolerance curve in response to power qualityevents identified in these captured voltage and/or current signals, themethod may return to block 4505. Alternatively, in embodiments in whichit is desirable to characterize power quality events identified in asingle set of captured voltage and/or current signals, the method mayend.

Further, in embodiments the event information may be used to adjust(e.g., expand) the “no interruption in function” region, for example, togenerate a custom tolerance curve for the specific IED location (similarto FIG. 2). It is to be appreciated that characterizing the electricalsystem at certain points is extremely useful to users because they canbetter understand the behavior of their system.

In some embodiments, the range indicated in the tolerance curve is apredetermined range, for example, a user configured range. In otherembodiments, the range is not predetermined. For example, I may chooseto have no “no interruption in function” region and say anythingdeviating from a nominal voltage needs to be evaluated. In this case,the voltage may range all over the place and I may have dozens of powerquality events; however, my load may not experience any interruptions.Thus, these events are not considered impactful. In this case, Iwiden/expand my “no interruption” region from basically the nominalvoltage outwards to the point where these events do start to perturbatemy loads (based on measured load impact pre-event vs. post event).

In other words, the invention is not limited to the ITIC curve (or anyother predetermined range or curve(s)). Rather, embodiments of theinvention call for “creating” a custom voltage tolerance curve for aspecific location (i.e., where the IED is located) within the electricalsystem or network. The curve may be based on the ITIC curve, the SEMIcurve, or any number of other curves. Additionally, the curve may be acustom curve (i.e., may not be based on a known curve, but rather may bedeveloped without an initial reference or baseline). It is understoodthat a predetermined tolerance curve is not required for this invention,rather it just used to explain the invention (in connection with thisfigure, and in connection with figures described above and below).

At block 4530, which is optional is some embodiments, an actionaffecting at least one component of the electrical system may beautomatically performed in response to the determined impact of the atleast one identified power quality event being outside of the rangeindicted in the tolerance curve. For example, in some embodiments acontrol signal may be generated in response to the determined impact ofthe at least one identified power quality event being outside of therange, and the control signal may be used to affect the at least onecomponent of the electrical system. In some embodiments, the at leastone component of the electrical system corresponds to at least one ofthe loads monitored by the IED. The control signal may be generated bythe IED, a control system, or another device or system associated withthe electrical system. As discussed in figures above, in someembodiments the IED may include or correspond to the control system.Additionally, in some embodiments the control system may include theIED.

As another example, an action that may be affected at block 4530 isstarting and stopping a timer to quantify a length (or duration) of theimpact to production, for example, in a facility with which the impactis associated. This will help a user make better decisions regardingoperation of the facility during atypical conditions.

Subsequent to block 4530, the method may end in some embodiments. Inother embodiments, the method may return to block 4505 and repeat again(for substantially the same reasons discussed above in connection withblock 4525). In some embodiments in which the method ends after block4530, the method may be initiated again in response to user input and/ora control signal, for example.

Referring to FIG. 46, a flowchart illustrates an example method 4600 forquantifying power quality events (or disturbances) in an electricalsystem that can be implemented, for example, on a processor of an IED(e.g., 121, shown in FIG. 1A) and/or a processor of a control system.Method 4600 may also be implemented remote from the IED in a gateway,cloud, on-site software, etc. This method 4600 evaluates voltage and/orcurrent signals measured and captured by the IED to determine whetherthe electrical system was impacted (e.g., at the IED(s) level) usingpre-event/post-event power characteristics. In embodiments, it ispossible to determine a recovery time using a threshold (e.g., thepost-event power is 90% of the pre-event power). This allows us toquantify the impact of a power quality disturbance to a load(s),process(es), system(s), facility(ies), etc.

As illustrated in FIG. 46, the method 4600 begins at block 4605, wherevoltage and/or current signals (or waveforms) are measured and capturedby an IED.

At block 4610, the voltage and/or current signals are processed toidentify a power quality event associated with one or more loads (e.g.,111, shown in FIG. 1A) monitored by the IED. In some embodiments,pre-event, event and post-event logged data may also be used to identifythe power quality event. The pre-event, event and post-event logged datamay, for example, be stored on a memory device associated with the IEDand/or gateway, cloud and/or on-site software application.

At block 4615, pre-event parameters are determined from the voltageand/or current signals. In embodiments, the pre-event parameterscorrespond to substantially any parameters that can be directly measuredand/or derived from voltage and current including, but not limited to,power, energy, harmonics, power factor, frequency, event parameters(e.g., time of disturbance, magnitude of disturbance, etc.), etc. Inembodiments, pre-event data can also be derived from “statisticalnorms.” Metadata may also be used to help derive additional parametersaccordingly.

At block 4620, an impact of the power quality event is determined,measured or calculated. In embodiments, the event impact is calculatedbased on pre-event vs. post-event parameters. In embodiments, thisincludes both the characteristics of the event (i.e., magnitude,duration, disturbance type, etc.) and its impact to load(s),process(es), system(s), facility(ies), etc. at the metered point in thesystem.

At block 4625, recovery thresholds (or conditions) are compared toreal-time parameters. In embodiments, the recovery thresholds maycorrespond to a percent of pre-event conditions to be considered as asystem, sub-system, process, and/or load recovery condition. Inembodiments, industry standards, market segment recommendations,historical analysis, independently determined variables, and/or loadcharacteristics may be used to provide the recovery thresholds.Additionally, statistical norms may be used to provide the recoverythresholds. In embodiments, the recovery thresholds are configured(e.g., pre-configured) recovery thresholds that are stored on a memorydevice associated with the IED. An alternative approach is to pass allvoltage event information to the cloud or on-site software and thenfilter it there using recovery thresholds. In this case, the recoverythresholds would be stored in the cloud or on-site and not in the IED.

At block 4630, the IED determines if the real-time parameters meet therecovery thresholds (or conditions). If the IED determines that thereal-time parameters meet the recovery thresholds, the method proceedsto block 4635. Alternatively, if the IED determines that the real-timeparameters do not meet the recovery thresholds, the method may return toblock 4625, and block 4625 may be repeated again. In embodiments, theoutput here is to determine the recovery time; therefore, it may stay inthe loop until the post-event levels meet a predetermined threshold.

At block 4635, the IED calculates a recovery time from the power qualityevent. In embodiments, the recovery time is calculated from a timeassociated with the power quality event (e.g., an initial occurrence ofthe power quality event) until a time the recovery thresholds are met.

At block 4640, an indication of the power quality disturbance (or event)is provided at an output of the IED. In embodiments, the indication mayinclude one or more reports and/or one or more control signals. Thereport may be generated to include information from any discrete IED ofthe electrical system including: recovery time, impact on power, costsassociated with the event impact, I/O status changes, time of event/timeof recovery, changes in voltages/currents, imbalance changes, areasimpacted, etc. In embodiments, recovery time and impact may be based ondata from one or more IEDs. The reports may be provided to customer,sales teams, offer management, engineering teams, and/or any otherinterested party, etc. The control signals may be generated to controlone or more parameters or characteristics associated with the electricalsystem. As one example, the control signals may be used to adjust one ormore parameters associated with load(s) which the IED is configured tomonitor.

At block 4640, the indication of the power quality disturbance (andother data associated with method 4600) may also be stored. In someembodiments, the indication may be stored locally, for example, on asame site as the IED (or on the IED device itself). Additionally, insome embodiments the indication may be stored remotely, for example, inthe cloud and/or on-site software. After block 4640, the method 4600 mayend.

Referring to FIG. 47, a flowchart illustrates an example method 4700 forexpanded qualified lead generation for power quality. Similar to method4600 described above in connection with FIG. 46, for example, inembodiments method 4700 can be implemented on a processor of an IEDand/or a processor of a control system. Method 4700 may also beimplemented remote from the IED in a gateway, cloud, on-site software,etc. In embodiments, by evaluating pre-event/post-event powercharacteristics of power quality events, it is possible to quantify thesusceptibility of the electrical system at metered points to powerquality disturbances. This information could be used to identify productofferings for mitigative solutions and provide better qualified leads toorganizations marketing those solutions. In embodiments, method 4700 mayalso be used for energy savings opportunities (e.g., power factorcorrection, increased equipment efficiency, etc.) when a power qualityevent occurs.

As illustrated in FIG. 47, the method 4700 begins at block 4705, wherevoltage and/or current signals (or waveforms) are measured and capturedby an IED.

At block 4710, the voltage and/or current signals are processed toidentify a power quality event associated with one or more loadsmonitored by the IED. In some embodiments, pre-event, event andpost-event logged data may also be used to identify the power qualityevent. The pre-event, event and post-event logged data may, for example,be stored on a memory device associated with the IED and/or gateway,cloud and/or on-site software application.

At block 4715, pre-event parameters are determined from the voltageand/or current signals. In embodiments, the pre-event parameterscorrespond to substantially any parameters that can be directly measuredand/or derived from voltage and current including, but not limited to,power, energy, harmonics, power factor, frequency, event parameters(e.g., time of disturbance, magnitude of disturbance, etc.), etc. Inembodiments, pre-event data can also be derived from “statisticalnorms.” Metadata may also be used to help derive additional parametersaccordingly.

At block 4720, an impact of the power quality event is calculated. Inembodiments, the event impact is calculated based on pre-event vs.post-event parameters. In embodiments, this includes both thecharacteristics of the event (i.e., magnitude, duration, disturbancetype, etc.) and its impact to load(s), process(es), system(s),facility(ies), etc. at the metered point in the system.

At block 4725, event characteristics are compared to mitigativesolutions (e.g., product solutions). In embodiments, there may be alibrary of design and applications criteria for solutions to mitigateissues associated with a power quality event or disturbance. The libraryof design and applications criteria for solutions may be stored on amemory device associated with the IED, or accessed by the IED (e.g.,remotely, via the cloud). In some embodiments, block 4725 may beperformed in the cloud or on-site software. That way the energy consumeris able to see everything from a system level.

At block 4730, the IED determines if a particular entity (e.g.,Schneider Electric) provides a mitigative solution for specific event.If the IED determines that the particular entity provides a mitigativesolution for the specific event, the method proceeds to a block 4635.Alternatively, if the IED determines that the particular entity does notprovide a mitigative solution for the specific event, the methodproceeds to a block 4750. In some embodiments, the “IED” may be definedas being in the cloud or on-site (yet remote from the meter). Inembodiments, it may be prudent to put the solutions and much of theanalysis in the cloud or on-site software because it's easier to update,the energy consumer has easier access to it, and it provides anaggregate system view.

At block 4735, a list of solutions provided by the particular entity isbuilt for the specific event or issue (or type of event or issue). Atblock 4740, a report is generated and provided to customers, sales teamsassociated with the particular entity or other appropriaterepresentatives of the entity. In embodiments, the report may includeinformation from any discrete metering device (or as a system)including: recovery time, impact on power, I/O status changes, time ofevent/time of recovery, changes in voltages/currents, changes in phasebalance, processes and/or areas impacted, etc. Report may includeinformation on SE solution (e.g., customer facing literature, featuresand benefits, technical specifications, cost, etc.), approximatesolution size required for given event (or event type), comparisons toexternal standards, placement, etc. Electrical and/or metering systemhierarchy and/or other metadata (e.g., load characteristics, etc.) maybe used to assist evaluation.

At block 4745, the report (and other information associated with themethod 4700) may be stored. In some embodiments, the report may bestored locally, for example, on a same site as the IED (or on the IEDdevice itself). Additionally, in some embodiments the report may bestored remotely, for example, in the cloud. In embodiment, blocks 4740and 4745 may be performed substantially simultaneously.

Returning now to block 4730, if it is determined that the particularentity does not provide a mitigative solution for the specified event,the method proceeds to a block 4750. At block 4750, event parametersand/or characteristics (and other information associated with the method4700) may be stored (e.g., locally and/or in the cloud). At block 4755,a report is generated based, at least in part, on select informationstored at block 4750. In embodiments, the report may include anevaluation of energy consumer impacts and needs for potential futuresolution development, third-party solutions, etc. After block 4755 (orblocks 4740/4745), the method 4700 may end.

Referring to FIG. 48, a flowchart illustrates an example method 4800 fordynamic tolerance curve generation for power quality. Similar to methods4500, 4600 and 4700 described above, in embodiments method 4800 can beimplemented on a processor of an IED and/or a processor of a controlsystem. Method 4800 may also be implemented remote from the IED in agateway, cloud, on-site software, etc. In embodiments, by evaluatingpre-event/event/post-event power characteristics of power qualityevents, it is possible (over time) to automatically develop a customevent tolerance curve for substantially any given energy consumer. Thisis extremely useful to help energy consumers identify, characterize,analyze and/or desensitize their system to power quality events.

As illustrated in FIG. 48, the method 4800 begins at block 4805, wherevoltage and/or current signals (or waveforms) are measured and capturedby an IED.

At block 4810, the voltage and/or current signals are processed toidentify a power quality event associated with one or more loadsmonitored by the IED. In some embodiments, pre-event, event andpost-event logged data may also be used to identify the power qualityevent. The pre-event, event and post-event logged data may, for example,be stored on a memory device associated with the IED and/or gateway,cloud and/or on-site software application.

At block 4815, pre-event parameters are determined from the voltageand/or current signals. In embodiments, the pre-event parameterscorrespond to substantially any parameters that can be directly measuredand/or derived from voltage and current including, but not limited to,power, energy, harmonics, power factor, frequency, event parameters(e.g., time of disturbance, magnitude of disturbance, etc.), etc. Inembodiments, pre-event data can also be derived from “statisticalnorms.” Metadata may also be used to help derive additional parametersaccordingly.

At block 4820, an impact of the power quality event is determined. Inembodiments, the event impact is calculated based on pre-event vs.post-event parameters. In embodiments, this includes both thecharacteristics of the event (i.e., magnitude, duration, disturbancetype, etc.) and its impact to load(s), process(es), system(s),facility(ies), etc. at the metered point in the system.

At block 4825, disturbance thresholds (or conditions) are compared tothe determined impact of the event. In embodiments, the disturbancethresholds may correspond to a percent change between pre-event andpost-event conditions to be considered a “significant” system,sub-system, process, and/or load disturbance. For example, a 5%reduction in load due to an electrical (or other) event may beconsidered “significant.” In embodiments, the disturbance thresholds areconfigured (e.g., pre-configured) disturbance thresholds that are storedon a memory device associated with the IED and/or gateway, cloud and/oron-site software application.

At block 4830, the IED determines if the system, sub-system, process,facility and/or load experienced (or is experiencing) a “significant”disturbance (e.g., based on the comparison at block 4825). If the IEDdetermines that the system, sub-system, process, facility and/or load(s)experienced a “significant” disturbance, the method proceeds to a block4835. Alternatively, if the IED determines that the system, sub-system,process, facility and/or load(s) has not experienced a “significant”disturbance, the method proceeds to a block 4840.

At block 4835, a disturbance point is generated and plotted asperturbative (e.g., impacting the system, sub-system, process, facilityand/or load(s), for example). At block 4845, a baseline tolerance curve(e.g., SEMI-F47, ITIC, CBEMA, etc.) is modified, changed and/orcustomized) based on characteristics associated with the specificrecorded disturbance (here, at block 4835).

Alternatively, at block 4840, in response to the IED determining thatthe system, sub-system, process, facility and/or load has notexperienced a “significant” disturbance, a disturbance point isgenerated and plotted as non-perturbative (e.g., not impacting thesystem, sub-system, process, facility and/or load(s), for example). Atblock 4845, the baseline tolerance curve is modified, changed and/orcustomized based on the characteristics associated with specificrecorded disturbance (here, at block 4840). For example, lines in thecurve may be moved between “no interruption region” and “nodamage/prohibited region.” Alternatively, the lines in the curve may notbe moved at all.

At block 4850, a report is generated. In embodiments, the report mayinclude information from substantially any discrete IED (or as a system)including: recovery time, impact on power, I/O status changes, time ofevent/time of recovery, changes in voltages/currents, imbalance changes,areas and loads impacted, etc. The report may include updated graphs oftolerance curve(s), highlighted changes in curve(s), recommendedmitigation solution(s), etc.

At block 4855, which is optional in some embodiments, at least one alarmsetting may be updated at discrete metering point(s) to match the newtolerance curve (e.g., generated at block 4845). At block 4860, the newtolerance curve (and other information associated with the method 4800)may be stored (e.g., locally, in a gateway, on-site software, and/or inthe cloud). In some embodiments, two or more of blocks 4850, 4855 and4860 may be performed substantially simultaneously. After blocks 4850,4855, and 4860, the method 4800 may end.

In general, equipment (e.g., a load or other electrical infrastructure)is designed to have a rated voltage and recommended operational range,as illustrated in FIGS. 49 and 50. The rated voltage is the desiredvoltage magnitude/level for optimal equipment operation. Additionally,the recommended operational range is the area surrounding the ratedvoltage (above and below the rated voltage) where the equipment maystill successfully operate continuously, although not necessarilyoptimally (e.g., lower efficiency, additional heating, higher currents,etc.). IED voltage event alarm thresholds (also referred to herein as“alarm thresholds” for simplicity) are typically configured (but notalways) to align with the recommended operational range so thatexcursions beyond the recommended operational range may be measured,captured and stored. This is because a strong correlation exists withexcessive voltage excursions and temporary or permanent damage to theequipment experiencing these excursions. Additionally, voltageexcursions may lead to operational issues, interruptions, loss of data,and/or any other number of impacts to equipment, processes, and/oroperations.

While the “recommended operational range” of loads, processes, and/orsystems is typically associated with a voltage magnitude, the durationof these excursions is also an important consideration. For example, a1-millisecond voltage excursion of +10% outside of the recommendedoperational range may not adversely impact the operation of a load,process, and/or system, nor impact its expected operational life.Alternatively, a 20-millisecond voltage excursion of +10% outside of therecommended operational range may cause the same load, process, and/orsystem to experience an interruption and/or reduce its life expectancy(by some extent).

FIGS. 49 and 50 illustrate two representations of the same concept.Namely, FIG. 49 shows a rms waveform and FIG. 50 shows an instantaneouswaveform. The rms waveform shown in FIG. 49 is derived from theinstantaneous waveform data shown in FIG. 50 using a well-known equation(root-mean-square) calculation. Both waveform representations are usefulfor analyzing power and energy-related issues and troubleshooting powerquality problems. Each respective graphic illustrates an exemplaryvoltage rating, upper alarm threshold, and lower alarm threshold for atheoretical load, process and/or system. In this case, the recommendedoperational range (shaded area) is assumed to align with the bounds ofthe upper and lower alarm thresholds, respectively.

Referring to FIG. 51, a flowchart illustrates an example method 5100 forcharacterizing power quality events in an electrical system that can beimplemented, for example, on a processor of at least one IED (e.g., 121,shown in FIG. 1A). Method 5100 may also be implemented remote from theat least one IED in a gateway, cloud-based system, on-site software, oranother head-end system in some embodiments.

As illustrated in FIG. 51, the method 5100 begins at block 5105, whereenergy-related signals are measured and data is captured, collected,stored, etc. by at least one first IED of a plurality of IEDs in anelectrical system. The at least one first IED is installed at a firstmetering point (e.g., a physical metering point) in the electricalsystem (e.g., metering point M₁, shown in FIG. 30F).

At block 5110, energy-related signals are measured and data is captured,collected, stored, etc. by at least one second IED of the plurality ofIEDs in the electrical system. The at least one second IED is installedat a second metering point (e.g., a physical metering point) in theelectrical system (e.g., metering point M₂, shown in FIG. 30F).

In some embodiments, the energy-related signals captured by the at leastone first IED and the energy-related signals captured by the at leastone second IED include at least one of: voltage, current, energy, activepower, apparent power, reactive power, harmonic voltages, harmoniccurrents, total voltage harmonic distortion, total current harmonicdistortion, harmonic power, individual phase currents, three-phasecurrents, phase voltages, and line voltages.

At block 5115, electrical measurement data for at least one firstvirtual meter in the electrical system is derived from (a) electricalmeasurement data from or derived from the energy-related signalscaptured by the at least one first IED, and (b) electrical measurementdata from or derived from the energy-related signals captured by the atleast one second IED. In embodiments, the at least one first virtualmeter is derived or located at a third metering point in the electricalsystem (e.g., metering point V₁, shown in FIG. 30F). In embodiments, thethird metering point (e.g., a virtual metering point) is different fromboth the first metering point and the second metering point.

In some embodiments, the electrical measurement data for the at leastone first virtual meter may be derived based on a known location of theat least one first virtual meter with respect to the at least one firstIED and the at least one second IED. For example, as described above inconnection with FIGS. 30B-30I, the electrical measurement data for avirtual meter (e.g., the at least one first virtual meter) may bederived based on known locations of, and parent-child relationship(s)between, the virtual meter and other meters (e.g., IEDs) in theelectrical system.

At block 5120, the derived electrical measurement data for the at leastone first virtual meter is used to generate or update a dynamictolerance curve associated with the third metering point or location. Asdiscussed in connection with figures above, a dynamic tolerance curvemay characterize an impact of a power quality event (or power qualityevents) in an electrical system. As also discussed in connection withfigures above, in some embodiments at least one means for mitigating theimpact of the power quality event (or power quality events) may beselected and applied in response to an analysis of the dynamic tolerancecurve.

Subsequent to block 5120, the method 5100 may end in some embodiments.In other embodiments, the method 5100 may repeat again, for example, inresponse to a control signal or user input, or automatically to ensurethat the dynamic tolerance curve associated with the third meteringpoint or location is up-to-date.

In some embodiments, the electrical measurement data from or derivedfrom energy-related signals captured by the at least one first IED mayalso be used to generate or update a dynamic tolerance curve associatedwith the first metering point. Additionally, in some embodiments theelectrical measurement data from or derived from energy-related signalscaptured by the at least one second IED may also be used to generate orupdate a dynamic tolerance curve associated with the second meteringpoint.

For example, in some embodiments the electrical measurement data from orderived from the energy-related signals captured by the at least onefirst IED in the electrical system may be processed to identify a powerquality event at the first metering point, and to determine an impact ofthe identified power quality event at the first metering point. Theidentified power quality event and the determined impact of theidentified power quality event at the first metering point may be usedto generate or update the first dynamic tolerance curve associated withthe first metering point. In some embodiments, the first dynamictolerance curve characterizes at least an impact of power qualityevent(s) on the first metering point.

The at least one first IED may be configured to monitor one or moreloads in the electrical system in some embodiments. In theseembodiments, the first dynamic tolerance curve may further characterizea response of the one or more loads to the power quality events.

In some embodiments, the at least one second IED may not be configuredto capture the power quality event, or the at least one second IED maybe incapable of capturing the power quality event. In these embodiments,for example, an impact of the identified power quality event at thesecond metering point may be determined based on an evaluation of theelectrical measurement data from or derived from the energy-relatedsignals captured by the at least one second IED proximate to a time ofoccurrence of a power quality event identified at the first meteringpoint. The time of occurrence of the identified power quality event atthe first metering point may be determined, for example, by processingthe electrical measurement data from or derived from the energy-relatedsignals captured by the at least one first IED.

In some embodiments, the identified power quality event and thedetermined impact of the identified power quality event at the secondmetering point may be used to generate or update the second dynamictolerance curve associated with the second metering point. In someembodiments, the second dynamic tolerance curve characterizes at leastan impact of power quality event(s) on the second metering point.

The at least one second IED may be configured to monitor one or moreloads in the electrical system in some embodiments. In theseembodiments, the second dynamic tolerance curve may further characterizea response of the one or more loads to the power quality events.

In the above-described embodiments in which the at least one second IEDmay not be configured to capture the power quality event, or the atleast one second IED may be incapable of capturing the power qualityevent, at least the determined time of occurrence of the identifiedpower quality event at the first metering point may be communicated fromthe at least one first IED to at least one of: a cloud-based system,on-site software, a gateway, and another head-end system. The impact ofthe identified power quality event at the second metering point may bedetermined on the at least one of: the cloud-based system, the on-sitesoftware, the gateway, and the other head-end system in someembodiments.

In some embodiments, communicating the determined time of occurrencefrom the at least one first IED to the at least one of: the cloud-basedsystem, the on-site software, the gateway, and the other head-endsystem, includes: producing at least one of a timestamp, alarm, and atrigger indicative of the determined time of occurrence on the at leastone first IED; and communicating the at least one of the timestamp, thealarm, and the trigger to the at least one of: the cloud-based system,the on-site software, the gateway, and the other head-end system.

Referring to FIG. 52, a flowchart illustrates an example method 5200 forcharacterizing an impact of a power quality event on an electric system.The method 5200 may be implemented, for example, on a processor of atleast one IED (e.g., 121, shown in FIG. 1A) and/or remote from the atleast one IED in at least one of: a cloud-based system, on-sitesoftware, a gateway, or another head-end system.

As illustrated in FIG. 52, the method 5200 begins at block 5205, whereenergy-related signals (or waveforms) are measured and data is captured,collected, stored, etc. by at least one metering device in an electricalsystem. In some embodiments, the at least one metering device includesat least one of an IED and/or a virtual meter. Additionally, in someembodiments the energy-related signals include at least one of: voltage,current, energy, active power, apparent power, reactive power, harmonicvoltages, harmonic currents, total voltage harmonic distortion, totalcurrent harmonic distortion, harmonic power, individual phase currents,three-phase currents, phase voltages, and line voltages.

At block 5210, electrical measurement data from or derived from theenergy-related signals captured by the at least one metering device atblock 5205, is processed to identify a power quality event associatedwith at least one load (e.g., 111, shown in FIG. 1A) monitored by the atleast one metering device. The at least one metering device and the atleast one load are installed at respective locations in the electricalsystem.

At block 5215, it is determined if the identified power quality eventhas an impact on the at least one load or on the electrical system. Ifit is determined the identified power quality event has an impact on theat least one load or on the electrical system, the method proceeds toblock 5220.

At block 5220, a recovery time for the at least one load or theelectrical system to recover from the identified power quality event isdetermined. Additionally, at block 5225, the data captured, collected,and/or stored during the recovery time is tagged (or otherwiseindicated) as atypical or abnormal based on the determined recoverytime. In some embodiments, the recovery time is tagged (or otherwiseindicated) in a dynamic tolerance curve associated with the at least oneload or the electrical system.

Returning briefly now to block 5215, if it is determined the identifiedpower quality event does not have an impact on the at least one load oron the electrical system, the dynamic tolerance curve may be updated andthe method may end in some embodiments. Subsequent to block 5225, themethod may also end in some embodiments.

In other embodiments, the method may include one or more additionalsteps. For example, in some embodiments in response to determining thatthe identified power quality event has an impact on the at least oneload or on the electrical system, one or more metrics associated withthe electrical measurement data may be compared against local utilityrate structures to calculate a total energy-related cost of theidentified power quality event, and to identify opportunities forreducing the total energy-related cost. It is understood that in someembodiments the total energy-related cost of the identified powerquality event, and the identified opportunities for reducing the totalenergy-related cost are based on the tagged recovery time data.

Additionally, in some embodiments an economic impact of the identifiedpower quality event may be determined based, at least in part, on one ormore metrics associated with the determined recovery time. Examplemetrics are discussed throughout this disclosure.

Referring to FIG. 53, a flowchart illustrates an example method 5300 forreducing recovery time from a power quality event in an electricalsystem, for example, by tracking a response characteristic of theelectrical system. The method 5300 may be implemented, for example, on aprocessor of at least one IED (e.g., 121, shown in FIG. 1A) and/orremote from the at least one IED in at least one of: a cloud-basedsystem, on-site software, a gateway, or another head-end system.

As illustrated in FIG. 53, the method 5300 begins at block 5305, whereenergy-related signals (or waveforms) are measured and data is captured,collected, stored, etc. by at least one IED in an electrical system. Insome embodiments, the energy-related signals include at least one of:voltage, current, energy, active power, apparent power, reactive power,harmonic voltages, harmonic currents, total voltage harmonic distortion,total current harmonic distortion, harmonic power, individual phasecurrents, three-phase currents, phase voltages, and line voltages.

At block 5310, electrical measurement data from or derived from theenergy-related signals captured by the at least one IED at block 5305,is processed to identify a power quality event associated with one ormore portions of the electrical system.

At block 5315, at least one means for recovering from the identifiedpower quality event is determined. Additionally, at block 5320 aselected one of the at least one means for recovering from theidentified power quality event is applied.

At block 5325, a response characteristic of the electrical system istracked in response to the selected one of the at least one means forrecovering from the identified power quality event being applied. Insome embodiments, the response characteristic of the electrical systemis tracked with respect to a baseline response of the electrical system.In some embodiments, tracking the response characteristic includesidentifying recurring event data. The identified recurring event datamay be used, for example, to forecast power quality events in theelectrical system.

As discussed above in connection with section VI of this disclosure,entitled “Disaggregation of Typical and Atypical Operational Data UsingRecovery Time,” it is important to recognize a facility's operationduring a recovery period is often aberrant or atypical as compared tonon-recovery times (i.e., normal operation). Additionally, it is usefulto identify and “tag” (i.e., denote) and differentiate aberrant oratypical operational data from normal operational data (i.e.,non-recovery data) for performing calculations, metrics, analytics,statistical evaluations, and so forth. Metering/monitoring systems donot inherently differentiate aberrant operational data from normaloperational data. Differentiating and tagging operational data as eitheraberrant (i.e., due to being in recovery mode) or normal providesseveral advantages, examples of which are provided in section VI of thisdisclosure.

At block 5330, the response characteristic of the electrical system isevaluated to determine effectiveness of the selected one of the at leastone means for recovering from the identified power quality event. If itis determined that the selected one of the at least one means forrecovering from the identified power quality event is not effective, themethod may return to block 5315 in some embodiments. Upon returning toblock 5315, at least one other means for recovering from the identifiedpower quality event may be determined. Additionally, at block 5320 aselected one of the at least one other means for recovering from theidentified power quality event may be applied.

Returning now to block 5330, if it is alternatively determined that theselected one of the at least one means for recovering from theidentified power quality event is effective, the method may end in someembodiments. Alternatively, information about the issue and itsresolution may be included in and/or appended to a history file, forexample. In other embodiments, the method may return to block 5325(e.g., such that the response characteristic of the electrical systemmay be further tracked).

As discussed in connection with figures above, an electrical system mayinclude a plurality of metering points or locations, for example,metering points M₁, M₂, etc. shown in FIGS. 29, 30A, 30B, and 30E-33. Insome embodiments, it may be desirable to generate, update or derive adynamic tolerance curve for each metering point of the plurality ofmetering points. Additionally, in some embodiments it may be desirableto analyze power quality events in the electrical system, for example,based on analysis of data aggregated, extracted and/or derived from thedynamic tolerance curves.

Referring to FIG. 54, a flowchart illustrates an example method 5400 foranalyzing power quality events in an electrical system. The method 5400may be implemented, for example, on a processor of at least one meteringdevice (e.g., 121, shown in FIG. 1A) and/or remote from the at least onemetering device in at least one of: a cloud-based system, on-sitesoftware, a gateway, or another head-end system. In some embodiments,the at least one metering device may be located at a respective meteringlocation of a plurality metering locations in the electrical system(e.g., M₁, shown in FIG. 29).

As illustrated in FIG. 54, the method 5400 begins at block 5405, whereenergy-related signals (or waveforms) are measured and data is captured,collected, stored, etc. by at least one of a plurality of meteringdevices in an electrical system. The at least one of a plurality ofmetering devices may each be associated with a respective meteringlocation of a plurality of metering locations in the electrical system.For example, a first one of the plurality of metering devices may beassociated with a first metering location in the electrical system(e.g., M₁, shown in FIG. 29), a second one of the plurality of meteringdevices may be associated with a second metering location in theelectrical system (e.g., M₂, shown in FIG. 29), and a third one of theplurality of metering devices may be associated with a third meteringlocation in the electrical system (e.g., M₃, shown in FIG. 29).

In some embodiments, the plurality of metering devices include at leastone IED (e.g., 121, shown in FIG. 1A). Additionally, in some embodimentsthe energy-related signals measured by the plurality of metering devicesinclude at least one of: voltage, current, energy, active power,apparent power, reactive power, harmonic voltages, harmonic currents,total voltage harmonic distortion, total current harmonic distortion,harmonic power, individual phase currents, three-phase currents, phasevoltages, and line voltages.

At block 5410, electrical measurement data from or derived from theenergy-related signals captured by the plurality of metering devices atblock 5405, is processed to generate, update and/or derive at least oneof a plurality of dynamic tolerance curves using one or more of thetechniques disclosed herein, e.g., in connection with FIGS. 45 and 51.In some embodiments, the at least one of a plurality of dynamictolerance curves are generated, updated and/or derived for each of theplurality of metering locations in the electrical system. The pluralityof metering locations include at least one physical metering point(e.g., M₁ and M₂, shown in FIG. 30F). In some embodiments, the pluralityof metering locations may also include at least one virtual meteringpoint (e.g., V₁, shown in FIG. 30F).

Similar to the dynamic tolerance curves discussed throughout thisdisclosure, each of the plurality of dynamic tolerance curves generated,updated and/or derived at block 5410 may characterize and/or depict aresponse characteristic of the electrical system. More particularly,each of the plurality of dynamic tolerance curves may characterizeand/or depict a response characteristic of the electrical system at arespective metering point of the plurality of metering points in theelectrical system (e.g., M₁, M₂, etc., shown in FIG. 29). In embodimentsin which the plurality of metering points include at least one virtualmetering point, the dynamic tolerance curve for the at least one virtualmetering point may be derived from the dynamic tolerance curve data forat least one physical metering point, for example, as discussed above inconnection with FIG. 30F and other figures herein.

In some embodiments, one or more of the plurality of dynamic tolerancecurves may have associated thresholds or setpoints, for example, fortriggering alarms in the electrical system. The thresholds or setpointsmay include one or more upper alarm thresholds and/or one or more loweralarm thresholds in some embodiments, several examples of which aredescribed above in connection with FIGS. 49 and 50, for example. Inaccordance with various aspects of this disclosure, an alarm may betriggered in response to a power quality event being above one or moreupper alarm thresholds or below or below one or more lower alarmthresholds. Additionally, in accordance with various aspects of thisdisclosure, an action (or actions) may be taken in response to the alarm(or alarms) being triggered.

Example actions that may be performed in response to the alarm beingtriggered, e.g., due to an anomalous voltage condition, may include, forexample, reporting an anomalous voltage condition (e.g., through avoltage event alarm generated by at least one IED) and/or automaticallyperforming an action (or actions) affecting at least one component ofthe electrical system, e.g., starting a diesel generator, operating athrow-over or static switch, etc. Similar to method 4500 described abovein connection with FIG. 45, in some embodiments a control signal may begenerated in response to the anomalous voltage condition, and thecontrol signal may be used to affect the at least one component of theelectrical system (e.g., a load monitored by the at least one IED, or amitigative device such as a diesel generator, throw-over or staticswitch, etc.). The control signal may be generated by the at least oneIED, a control system, or another device, software or system associatedwith the electrical system. As discussed in figures above, in someembodiments the at least one IED may include or correspond to thecontrol system. Additionally, in some embodiments the control system mayinclude the at least one IED.

Further example actions that may be performed in response to the alarmbeing triggered, e.g., to prevent a potential tripping due to a voltagesag duration or magnitude, may include, for example, turning “off” somerunning process steps which are identified as potential “sagexacerbators” such as postponing (if possible) a motor start during anexisting voltage sag. Additionally, in some embodiments a user alarm(e.g., sent to a mobile phone application or other device application)may be triggered in response to the alarm (i.e., a system alarm) beingtriggered. Further, in some embodiments the alarm may be used as inputto a manufacturing SCADA system, for example, to change or delay anotherprocess. The alarm may also be remedial, for example, by providing themaintenance team with a precise localization of an issue and a prioritylist of what issue(s) is/are most critical, which issue(s) to addressfirst, second, etc. In some embodiments, the system may also provide arecommended response (or responses) to the issues, e.g., for correctingthe issues. One example recommended response may be to disconnect and/orreconfigure a load causing the issues, with the disconnecting and/orreconfiguring being manually performed by the user and/or automaticallyperformed by the system. As illustrated above, the types of actions thatcan be performed in response to alarms can take a variety of forms.

The above-discussed thresholds or setpoints (i.e., thresholds fortriggering alarms) may be generated during a so-called “learning period”in some embodiments, as discussed further below in connection with FIG.55, for example.

In accordance with some aspects of this disclosure, the dynamictolerance curves generated, updated and/or derived at 5410 can beprepared by deducing relevant signal characteristics from theabove-discussed electrical measurement data. Examples of relevant signalcharacteristics (and information) may include severity (magnitude),duration, power quality type (e.g., sag, swell, interruption,oscillatory transient, impulsive transient, etc.), time of occurrence,process(es) involved, location, devices impacted, relative or absoluteimpact, recovery time, periodicity of events or event types, etc.Additional examples of relevant signal characteristics may includeidentified changes in current, phase shifts, etc., which result in aninterpretation (or interpretations) such as “inductive or capacitiveload type added or removed from system,” “percentage of pre-event loadsadded or dropped from system” during an event, etc. In some embodiments,these relevant signal characteristics are determined from portions ofthe electrical measurement data from prior to a start time of powerquality events, and from portions of the electrical measurement datafrom after an end time of power quality events. Additionally, in someembodiments these relevant signal characteristics are determined fromportions of the electrical measurement data from during the powerquality events (such as magnitude, duration, phases impacted, etc.).

In some embodiments, at least one of the plurality of dynamic tolerancecurves may be displayed on a graphical user interface (GUI) (e.g., 230,shown in FIG. 1B) of at least one metering device, and/or or a GUI of acontrol system associated with the electrical system, for example. Insome embodiments, the at least one of the plurality of dynamic tolerancecurves is displayed in response to user input. The control system maycorrespond to or include a power or industrial/manufacturing SCADAsystem, a building management system, and a power monitoring system, asa few examples.

In some embodiments, the method may proceed to block 5415 after block5410. In other embodiments, the method may return to block 5405 andblocks 5405 and 5410 may be repeated, for example, for capturingadditional energy-related signals and updating the plurality of dynamictolerance curves generated at block 5410. In doing so, the plurality ofdynamic tolerance curves may be automatically/dynamically maintained attheir optimal level. It is understood that optimal may mean differentthings due to the nature of the loads, segments, processes, and/or otherinputs. This may result in a multitude of different curves, each or allrelevant to different usages (applications). According to some aspectsof this disclosure, “dynamic” of the dynamic tolerance curve means thatthere are changes which may occur over time. Thus, time may be animportant factor to model, or an important factor to include in anymodel or curve. This is core to the innovation at each meter, and thenat the global system level analysis, aggregation, action recommendationor any related automation changes or commands (non-exhaustive list).Thus, a model can be built of the evolution over time. To simplify theunderstanding, this may be represented by two (or more) different linkedgraphs, for example. A trend of each change point's optimal values (whatis the value of the “initial impact” point). Additionally, the relativeimpact on loads or critical loads, such as the percentages of loadsdropping off at an initial impact point, over time.

At block 5415, power quality data from the plurality of dynamictolerance curves is selectively aggregated. In some embodiments, thepower quality data is selectively aggregated based on locations of theplurality of metering points in the electrical system. For example,power quality data from dynamic tolerance curves associated withmetering points in a same or similar portion of the electrical systemmay be aggregated.

Additionally, in some embodiments the power quality data is selectivelyaggregated based on criticality or sensitivity of the plurality ofmetering points to power quality events. For example, power quality datafrom dynamic tolerance curves associated with highly critical orsensitive metering points may be aggregated. In some embodiments, thecriticality of the metering points is based on the load type(s) at themetering points, and the importance of the loads to operation of aprocess (or processes) associated with the electrical system.Additionally, in some embodiments the sensitivity of the metering pointsis based on the sensitivity of the loads to power quality events.

In accordance with some aspects of this disclosure, degradations orimprovements in the loads or the electrical system's sensitivity orresilience to power quality events may be identified at each respectivemetering point in the electrical system. The degradations orimprovements in the electrical system's sensitivity or resilience topower quality events may be identified, for example, based identifiedchanges in the dynamic tolerance curves for each respective meteringpoint. In some embodiments, the identified degradations or improvementsin the loads or the electrical system's sensitivity or resilience topower quality events may be reported. For example, the identifieddegradations or improvements in the loads or the electrical system'ssensitivity or resilience to power quality events may be reported bygenerating and/or initiating a warning (e.g., a key performanceindicator) indicating the identified degradations or improvements in theelectrical system's sensitivity or resilience to power quality events.The warning may be communicated via at least one of: a report, a text,an email, audibly, and an interface of a screen/display, for example. Insome embodiments, the screen/display may correspond to a screen/displayon which the dynamic tolerance curve(s) is/are presented. Thecommunicated warning may provide actionable recommendations forresponding to the identified degradations or improvements in theelectrical system's sensitivity or resilience to power quality events.

In accordance with further aspects of this disclosure, power qualitydata from the plurality of dynamic tolerance curves may be selectivelyaggregated to generate one or more aggregate dynamic tolerance curves.

In some embodiments, the plurality of dynamic tolerance curves may beaggregated by graphing the plurality of dynamic tolerance curves in acomposite plot (or plots). For example, the response characteristic ofthe electrical system at each metering point, e.g., as indicated in theplurality of tolerance curves, may be presented as a sub-graph in thecomposite plot (or plots). In some embodiments, the plurality oftolerance curves may be presented in the composite plot (or plots),e.g., in order of appearance in the plot (or plots), based on acriticality or sensitivity ranking (1^(st) to last) of the meteringpoints. It is understood that other groupings may be overlaid, forexample, to reflect an analysis by location. One may first group bylocation such as one graph per location, then sort the meters indecreasing order of sensitivity or criticality.

In accordance with some aspects, it is possible to overlay common impactmodel(s) and highlight key specificities of each meter by color-coding,for example. A dynamic tolerance curve generated for a most sensitivehigh-criticality (e.g., on magnitude initial start) meter in anelectrical system may, for example, serve as a reference curve for eachand all the other meters. Then it is possible to play on other graphicalcomponents to highlight the criticality of a load/process if tagged foreach or certain meters (for example by using the line thickness, orcoloring the curve line in red, green or blue for “highly-critical”,“somewhat-critical”, “not-critical”, etc.).

Additionally, in accordance with some aspects, dynamic tolerance curvesassociated with each relevant group of meters may be presented as asub-graph in the composite plot (or plots). The relevance of a group maydepend, for example, on the goal (or goals) of the composite plot (orplots). It is understood that many different relevant groupings may beused in different contexts. For example, the curves/meters may begrouped for location (e.g., meter location) based analysis.Additionally, the curves/meters may be grouped by criticality. Forexample, highly critical meters may be grouped vs. the other categoriesof meters. Additionally, all the individual highly-critical meters maybe plotted against an aggregated curve of less critical meters. Thecurves/meters may also be grouped by impact, e.g., group meters bysimilar percentages at a given magnitude of a voltage sag and durationlevel. Additionally, the meters may be grouped by sensitivity curve orrange, e.g., distance between initial and final impact.

It is understood that the above criteria may be combined. For example, afilter may be applied per location, so a grouping by location. Then thetypical location may be plotted where all meters at this location areused to calculate the mean thresholds and change points. Aggregatedtypical graphs of highly critical meters may then be compared with theminimal “initial impact values” curve from the typical (mean) “initialimpact values” curve. This would indicate whether there is one or a fewIEDs creating the “weak points” of the system that may need to be mademore robust (e.g., SagFigher® solution to be analyzed).

The ranking (first to last) in order of appearance of each meter may bebased on the criticality or the sensitivity of the electrical systemand/or the load (or loads) at the metering point(s) associated with themeter, for example. However, other groupings may be overlaid. Forexample, to reflect an analysis by location, one may first group themeters by location, then sort the meters in decreasing order ofsensitivity or criticality.

Subsequent to block 5415, the method may proceed to block 5420. At block5420, power quality events in the electrical system are analyzed basedon the selectively aggregated power quality data. For example, theselectively aggregated power quality data may be processed or analyzedto determine a relative criticality score of each of the power qualityevents to a process or an application associated with the electricalsystem. The relative criticality score may be based on an impact of thepower quality events to the process or the application, for example. Forexample, a rooftop HVAC unit or system dropping off for three hours foran office building may be considered less critical then for a datacenter. Additionally, a motor in a steel production line may beconsidered more critical than a lighting rail or fixture above the steelproduction line. In some embodiments, the relative criticality score maybe used as an input for supervised learning. Supervised learning meansthat some variable(s) may be used to teach the calculation engine whichissues have more value than others.

In some embodiments, the impact of the power quality events is relatedto tangible or intangible costs associated with the power quality eventsto the process or the application. Additionally, in some embodiments theimpact of the power quality events is related to relative impact onloads in the electrical system. In some embodiments, there may be anabsolute threshold or non-acceptable trip off-line of a load (or loads)related to the process or the application. For example, in the aboveexample of a data center, a cooling pump of the HVAC system may beconsidered a critical component, which should never be trip off-line.Additionally, in the above example of a steel production line, there maybe a path in the production line where there is no redundancy of motoron the conveyor belt. This may be the component or process which shouldnever misoperate, trip off-line, and/or malfunction.

In some embodiments, the determined relative criticality score, e.g., atblock 5420, may be used to prioritize responding to (or a response to)the power quality events. For example, the determined relativecriticality score (e.g., a criticality score using a historical analysisof the voltage event impacts) may be used to determine how significantload loss effects the system operation. The more significant the impact,the worse the relative criticality score as related to other IEDs in thesystem. This could also be performed based on discrete locations of saidIEDs (i.e., some locations are more significantly impacted by voltageevents than other locations). Some locations may inherently have worsecriticality scores than others due to their processes, functions, and/orloads.

The relative criticality score may also be used, for example, as aninput to drive a manufacturing SCADA to determine if an alternativeprocess should be triggered or terminated. For example, voltage sags mayresult in poorer product quality or product defects such as fluctuationsin the heat and pressure conditions producing an increase in thequantity of air bubbles in rubber products. A higher criticality scoremay reflect these portions of the production process. In this case, analternative/corrective process may be triggered to complement thecurrent process step or scrap the product.

In embodiments in which the power quality data from the plurality ofdynamic tolerance curves has been selectively aggregated to generate oneor more aggregate dynamic tolerance curves at block 5415, the aggregatedynamic tolerance curves may be analyzed or otherwise evaluated toanalyze power quality events in the electrical system.

The power quality events analyzed at block 5420 may include, forexample, at least one of: a voltage sag, a voltage swell, a voltagetransient, an instantaneous interruption, a momentary interruption, atemporary interruption, and a long-duration root-mean-square (rms)variation.

In some embodiments, at block 5420 it may also be determined if anydiscrepancies exist between the selectively aggregated power qualitydata. For example, the discrepancies may include inconsistent namingconventions in the selectively aggregated data. As one example, thenaming conventions for power quality data in a dynamic tolerance curveassociated with a first metering point in the electrical system may bedifferent from the naming conventions for power quality data in adynamic tolerance curve associated with a second metering point in theelectrical system. In embodiments in which discrepancies are identified,additional information may be requested (e.g., from a system user) toreconcile the discrepancies. Recommendations may be made.

After block 5420, the method may end in some embodiments. In otherembodiments, the method may return to block 5405 and repeat again (e.g.,for capturing additional energy-related signals, updating the dynamictolerance curves associated with the metering points in the electricalsystem, and selectively aggregating power quality data from the updateddynamic tolerance curves to analyze power quality events).

It is understood that method 5400 may include one or more additionalblocks in some embodiments. For example, the method 5400 may includetagging power quality events in one or more of the plurality of dynamictolerance curves with relevant and characterizing information, forexample, based on information extracted about the power quality events.In some embodiments, the relevant and characterizing informationincludes at least one of: a severity score, recovery time, percentage ofloads added or lost during event, inductive or capacitive load(s) addedor dropped during event, location within a system, type of load or loadsdownstream from the IED providing the information, impact to afacility's operation, etc. Additionally, in some embodiments theinformation about the power quality events may be extracted fromportions of electrical measurement data from energy-related signalscaptured prior to a start time of the power quality events. In someembodiments, the information about the power quality events may beextracted from portions of electrical measurement data fromenergy-related signals captured after an end time of the power qualityevents. Information may also be extracted from portions of electricalmeasurement data from energy-related signal captured during the powerquality events.

In accordance with some aspects of this disclosure, tagging and dataenriching can be performed at substantially any time. For example,tagging and data enriching may be performed before or during the sitecommissioning based on the loads which will be running, and based oncalculations and process planning (times of preparation and ofcalculations) as a few examples. During normal operations, events may betagged on the fly (times of normal running operations). Additionally,events may be tagged during re-commissioning or extensions, ormaintenance (e.g., times of changes).

As discussed in connection with figures above, a dynamic tolerance curvemay characterize an impact of a power quality event (or power qualityevents) in an electrical system. As also discussed in connection withfigures above, in some embodiments at least one means for mitigating theimpact of the power quality event (or power quality events) may beselected and applied in response to an analysis of the dynamic tolerancecurve. For example, in accordance with some aspects of this disclosure,the dynamic tolerance curve may be used to prevent tripping of morecritical loads by driving counter measures, including system levelprocess optimization. For illustration purposes, if a user specifiesabsolute thresholds, the system (e.g., control system) may calculate anoptimal curve to avoid the risk of tripping (the protection relay) forthis given meter. Additionally, the system may decide to turn offtypical loads which are known (or calculated) as possible causes of apower quality event (e.g., voltage sags), or cut first (faster) otherpotential process steps which may generate risks of increasing ormaintaining longer duration of the power quality event.

It is understood that one or more steps of method 5400 may be combinedwith one or more steps of other methods discussed throughout thisdisclosure, for example, for generating, updating and/or derivingdynamic tolerance curves.

Referring to FIG. 55, a flowchart illustrates an example method 5500 forgenerating dynamic tolerance curves in accordance with embodiments ofthis disclosure. In accordance with some embodiments of this disclosure,method 5500 is illustrative of examples steps that may be performed atblocks 5405 and 5410 of method 5400 discussed above in connection withFIG. 54. Similar to method 5400, method 5500 may be implemented on aprocessor of at least one metering device (e.g., 121, shown in FIG. 1A)and/or remote from the at least one metering device in at least one of:a cloud-based system, on-site software, a gateway, or another head-endsystem.

As illustrated in FIG. 55, the method 5500 begins at block 5505, where aso-called “learning period” begins. At block 5510, energy-relatedsignals (or waveforms) are measured and data is captured, collected,stored, etc. by a plurality of metering devices in an electrical system.In some embodiments, the data captured corresponds to measurement datarequired for the calculation of thresholds or setpoints at block 5515.

At block 5515, thresholds or setpoints (e.g., alarm thresholds) arecalculated based on the energy-related signals measured at block 5510.Additionally, in accordance with some embodiments of this disclosure,anticipatory thresholds (e.g., preventive thresholds) may be determinedat block 5515, e.g., as a percentage (e.g., 90%) of the regularthresholds to prevent damage to electrical system loads. Critical andnon-critical loads may have different anticipatory thresholds, forexample, based on their impact on the electrical system. For critical ormore sensitive loads, the anticipatory thresholds may be tighter orcloser to the nominal voltage. Additionally, for non-critical or lesssensitive loads, the anticipatory thresholds may be looser or fatherfrom the nominal voltage. For example, anticipatory thresholds may beset between a sag alarm threshold of ±10% of the nominal voltage and the“initial impact voltage threshold” (also called Initial impact point)for some “critical loads,” e.g., for a voltage sag for instance.Additionally, for some “non-critical loads,” the anticipatory thresholdsmay be set to 30% of loads lost, e.g., for a voltage sag. Examplethreshold calculations for individual meters in an electrical system areshown in the chart below.

Individual meters thresholds calculation Loads Loads Initial Loads Lossof lost Preventative Loads Sag Alarm lost Impact lost 50% loads (50%threshold lost Criticality threshold (Sag threshold (initial thresholdloads ((Alarm + (initial group (magnitude) alarm) (magnitude) impact)(magnitude) lost) Initial)/2) impact) Meter 1 High 90% 0% 70% 30% 50%50% 80% 0% Meter 2 High 90% 0% 65% 35% 55% 50% 78% 0% Meter 3 High 90%0% 60% 40% 60% 50% 75% 0% Meter 4 Medium 90% 0% 70% 30% 50% 50% 80% 0%Meter 5 Medium 90% 0% 65% 35% 55% 50% 78% 0% Meter 6 Medium 90% 0% 60%40% 60% 50% 75% 0% Meter 7 Low 90% 0% 70% 30% 50% 50% 80% 0% Meter 8 Low90% 0% 65% 35% 55% 50% 78% 0% Meter 9 Low 90% 0% 60% 40% 60% 50% 75% 0%

Additionally, example threshold calculations for groups of meters in anelectrical system, for example, based on criticality groups, are shownin the chart below.

Groups of meters thresholds calculation Loads Loads Initial Loads Lossof lost Preventative Loads Sag Alarm lost Impact lost 50% loads (50%threshold lost Criticality threshold (Sag threshold (initial thresholdloads ((Alarm + (initial group (magnitude) alarm) (magnitude) impact)(magnitude) lost) Initial)/2) impact) High 90% 0% 70% 30% 50% 50% 80% 0%Medium 90% 0% 70% 30% 50% 50% 80% 0% Low 90% 0% 65% 35% 55% 50% 78% 0%

As noted and illustrated above, meters may be grouped based oncriticality. For example, the meters may be grouped in “high,” “medium”and “low” criticality groups, e.g., based on whether the meters include,or are coupled to, critical or non-critical loads. Meters grouped in thehigh criticality group may have thresholds selected, for example, toprevent voltage sags. Additionally, meters grouped in the mediumcriticality group may have thresholds selected, for example, to triggerprocess changes. Further, meters grouped in the low criticality groupmay have thresholds selected, for example, such that HVAC capacity losscreates only over consumption and drop of comfort ° C. An example methodfor moving from the individual meters thresholds calculation to thegroups of meters thresholds calculation shown in the charts above isillustrated in FIG. 55A, for example.

In accordance with embodiments of this disclosure, the above-discussedthresholds may be adjusted (either manually, automatically, orsemi-automatically). For example, may the thresholds may be manuallyadjusted in response to usage and/or user/process impact analysis,

Additionally, the thresholds may be automatically adjusted, for example,in response to recovery duration, as illustrated in the chart below.

Referring now to block 5520, at block 5520 it is determined if thelearning period should be continued. For example, in some embodimentsthe learning period may have an associated (e.g., pre-programmed or userconfigured) time period, and once a time associated with the learningperiod reaches the associated time period it may be determined thatthere is no need to continue the learning period. In other embodiments,it may be determined if “sufficient” data has been obtained to generatethe thresholds or setpoints, and once sufficient data has been obtainedit may be determined that the there is no need to continue the learningperiod.

In some embodiments, during the learning period, events, process parts,or meters in an electrical system may be tagged, for example, based onmeasurement data collected during the learning period, e.g., at block5510.

If it is determined that the learning period should be continued, themethod returns to block 5510, and blocks 5510, 5515 and 5520 arerepeated. Alternatively, if it is determined that there is no need tocontinue the learning period, the method proceeds to block 5525.

At block 5525, which is similar to block 5405 of method 5400 in someembodiments, energy-related signals (or waveforms) are measured and datais captured, collected, stored, etc. by at least one of a plurality ofmetering devices (i.e., IEDs) in an electrical system. Additionally, atblock 5530, which is similar to block 5410 of method 5400 in someembodiments, electrical measurement data from or derived from theenergy-related signals captured by the plurality of metering devices atblock 5525, is processed to generate, update and/or derive at least oneof a plurality of dynamic tolerance curves. In some embodiments, the atleast one of a plurality of dynamic tolerance curves are generated,updated and/or derived for each of a plurality of metering points in theelectrical system.

After block 5525, the method may end in some embodiments. In otherembodiments, the method may return to block 5525 and repeat again (e.g.,for capturing additional energy-related signals, and updating thedynamic tolerance curves associated with the metering points in theelectrical system). In further embodiments, the method may return toblock 5505 and repeat again. More particularly, in accordance with someaspects, the learning period may be initiated again, for example, fromtime to time, in response to user input, etc.

Similar to method 5400, it is understood that method 5500 may includeone or more additional blocks in some embodiments. For example, in someembodiments the thresholds or setpoints calculated at block 5515 may bedefined or redefined after the above-discussed learning period.Additionally, similar to method 5400, it is understood that one or moresteps of method 5500 may be combined with one or more steps of othermethods discussed throughout this disclosure, for example, forcalculating the thresholds or setpoints and/or generating, updatingand/or deriving dynamic tolerance curves.

Referring to FIG. 56, a flowchart illustrates an example method 5600 fortagging criticality scores, for example, during a learning period, suchas the learning period discussed above in connection with FIG. 55.Similar to method 5500, method 5600 may be implemented on a processor ofat least one metering device (e.g., 121, shown in FIG. 1A) and/or remotefrom the at least one metering device in at least one of: a cloud-basedsystem, on-site software, a gateway, or another head-end system.

As illustrated in FIG. 56, the method 5600 begins at block 5605, wherecriticality scores are tagged for each metering device in an electricalsystem. As discussed through this disclosure, each metering device(e.g., IED) in an electrical system may be associated with a respectivemetering point in the electrical system, and electrical measurement datafrom energy-related signals captured by each metering device may be usedto generate a dynamic tolerance curve associated with a respectivemetering point. As also previously discussed, absolute or relativecriticality scores may be determined for metering devices, new powerquality events, etc. In accordance with some aspects of this disclosure,a user may tag criticality scores for each metering device in anelectrical system at block 5605. In accordance with other aspects ofthis disclosure, a system (e.g., a system for analyzing power qualityevents) may tag criticality scores for each metering device in anelectrical system at block 5605.

At block 5610, criticality scores for each new power quality event maybe tagged, in some embodiments by the system, and in other embodimentsby the user. Each new power quality event may be identified, forexample, using one or more techniques discussed in connection withfigures above (e.g., method 4500, shown in FIG. 45). At block 5615,information tags may be added for each new power quality event orcriticality score (such as criticality of a specific industrial orbuilding process running at event time). For example, tagged information(i.e., metadata, real data, etc.) may be appended/update for each newpower quality event. In short, new information may be optionallyappended to a device, process, and/or system as events occur.Criticality scores may also be changed and/or update at device(s),process(es), and/or system(s) as relevant new data comes in. In someembodiments, these information tags may correspond to system added orinferred information tags. The system added or inferred information tagsmay, for example, help to identify co-occurrences, possible sources,etc., such as power factor changes, source changed identification,inductive or resistive loads added or lost, capacitor bank switched “on”or “off,” and/or metadata from building management system or SCADAsystem, for example. Additionally, the system added or inferredinformational tags may indicate correlations between power qualityevents associated with different metering devices in an electricalsystem. The correlations being deduced, for example, from analysis ofthe power quality events using one or more techniques previouslydisclosed herein.

After block 5615, the method may end in some embodiments. In otherembodiments, the method may return to block 5605 and repeat again (e.g.,for tagging criticality scores for further new events, and/or updatingcriticality scores for metering devices). In further embodiments, themethod may include one or more additional steps, as will be appreciatedby one of ordinary skill in the art.

Referring to FIG. 57, a flowchart illustrates another example method5700 for tagging criticality scores, for example, during a learningperiod, such as the learning period discussed above in connection withFIG. 55. Similar to method 5500, method 5700 may be implemented on aprocessor of at least one metering device (e.g., 121, shown in FIG. 1A)and/or remote from the at least one metering device in at least one of:a cloud-based system, on-site software, a gateway, or another head-endsystem. In accordance with some aspects, method 5700 corresponds to datadriven system tagging of criticality scores per phase for each new powerquality event.

As illustrated in FIG. 57, the method 5700 begins at block 5705, where apercentage of loads lost in response to each new event is determined. Asdiscussed in connection with figures above, an electrical system mayinclude one or more loads, and a metering device (e.g., an IED) may beconfigured to monitor at least one of the one or more loads. In someembodiments, the percentage of loads lost may be determined byevaluating new initial impact thresholds and/or new final impactthresholds, and positions in between these thresholds (e.g.,distribution or function approximation), e.g., to interpolate a voltageevent's impact using different models. These different models, asillustrated in FIGS. 57A-57K, for example, as will be discussed furtherbelow, may be used to approximate how the electrical system responds toeach new power quality event.

In accordance with some embodiments, initial impact thresholds maycorrespond to thresholds for detecting a first percentage of loads lost.For example, one illustrative initial impact threshold or point maycorrespond to a threshold of detecting 20% of loads lost at 75% ofnominal voltage, e.g., for a specific time duration. In the aboveexample, the 20% of loads lost corresponds to the first percentage ofloads lost, and the initial impact point, as illustrated in FIG. 57A,for example.

In accordance with some embodiments, final impact thresholds maycorrespond to thresholds for detecting when all loads (i.e., 100% of theloads) are lost. For example, one illustrative final impact threshold orpoint may correspond to a threshold of detecting 100% of loads lost at55% of nominal voltage, e.g., for a specific time duration. In the aboveexample, the 100% of loads lost corresponds to the final impact point,as illustrated in FIG. 57A, for example.

Positions (or zones) in between the above-discussed initial impactthresholds and final impact thresholds may be determined, for example,using one or more models, as briefly discussed above. In accordance withone example linear model, as shown in FIGS. 57B and 57C, for example,between 75% and 55% of the nominal voltage, 4% of load loss for each 1%decrease in the nominal voltage. Additionally, in accordance with oneexample logarithmic (or log) model, as shown in FIGS. 57B-57D, forexample, between 75% and 55% of the nominal voltage, the log percentageof load loss for each 1% decrease in the nominal voltage.

In accordance with some embodiments, these inferred “curve model”functions can be determined by substantially any approximated or modeledline or curve calculation or curve fitting algorithm. Relevant modelscan be calculated and applied as a maximum function, as illustrated inFIGS. 57B and 57C, for example, where the curve represents the “normal”curve. It is understood that other statistical and inferred model typesmay be included in this approach, such as, for example, mean, median,min, loss, regression, and other calculations.

In applying these functions, such as illustrated in FIGS. 57C and 57Dand the table provided directly beneath this paragraph, the form of thecurve(s) may change due to an increasing stepwise function overlaid tothe “curve model” function. This is typical and reflects the closer anIED is to the terminus of the electrical system, the more distinct theload changes will be (here, maybe five different motors). Conversely,the closer an IED is to the source (e.g., utility delivery point), theless distinct load changes will typically be. Thus, the load profile mayappear to be more “continuous” due to the greater diversity of thedownstream loads in many cases, such as in our examples, there may beten (or any number of) different motors.

Linear Linear Linear Linear few many few many Linear load load Log loadload Magnitude continuous steps steps continuous steps steps 75 20 20 2020 20 20 74 24 20 20 45 20 20 73 28 20 28 58 20 58 72 32 20 28 65 20 5871 36 20 36 71 20 71 70 40 40 36 75 75 71 69 44 40 44 78 75 78 68 48 4044 81 75 78 67 52 40 52 83 75 83 66 56 40 52 85 75 83 65 60 60 60 87 8787 64 64 60 60 89 87 87 63 68 60 68 91 87 91 62 72 60 68 92 87 91 61 7660 76 93 87 93 60 80 80 76 95 95 93 59 84 80 84 96 95 96 58 88 80 84 9795 96 57 92 80 92 98 95 98 56 96 80 92 99 95 98 55 100 100 100 100 100100

It is understood the loads (e.g., motors) may be different sizes andhave various response characteristics. In the example given, largerloads may be much more sensitive to relays tripping (i.e., due tovoltage sags) than smaller loads. This would explain a log function suchas provided in the above-discussed example.

It is also understood that the above-discussed models and functions maybe based on, or adjusted based on, power quality event sensitivity. Forexample, as illustrated in FIGS. 57E and 57F, the loss of loads curvesmay be modeled based on event maximum sensitivity. Additionally, asillustrated in FIGS. 57G and 57H, the loss of loads curves may bemodeled based on event median sensitivity. Outliers from the curves maybe identified as most oversensitive, for example, as shown in FIG. 571.In some embodiments, the outliers may be identified as first candidatesfor correlation analysis, for example, to determine if they accuratelyreflect how the electrical system responds to power quality events.Referring briefly to FIG. 57J, it is understood that in some embodimentsthe above discussed thresholds (e.g., initial impact and final impactthresholds) may be used to identify 50% (or another percentage) of loadslost. Also referring briefly to FIG. 57K, curves for a plurality ofmeters (e.g., three meters) may be aggregated, for example, using themost sensitive meter of the plurality of meters.

Returning now to FIG. 57, at block 5710 of method 5700 the severity ofeach new power quality event may be determined. In some embodiments, theseverity is indicated in the form of a severity score which may bebased, for example, on a magnitude and a duration of the power qualityevent. The “severity score” could be based on relative values(percentage of total load on any particular device, including the devicemeasuring the voltage event) or absolute values (based on real measuredvalue in kW, kVA, and/or other parameter), with the durationcorresponding to how long the event lasted. One example way to calculatethe severity score includes combining the magnitude of the power qualityevent with the duration of the power quality event, i.e., the severityscore is a product of the magnitude and the duration. For example, theduration (e.g., real impact duration) of the power quality event may becombined with (e.g., multiplied by) the magnitude of voltage and/orcurrent measurements from prior to, during and/or after the powerquality event to determine the severity score. It is understood thatthere may be a number of other ways in which the severity score may bedetermined. For example, the severity score may be determined by orderived from at least one of power quality event magnitude, duration,recovery time, percentage of recovery loss, power quality eventlocation, etc. It is understood that different zones in an electricalsystem may have higher priority than other-zones. Additionally, it isunderstood that other scores may be determined in addition to theseverity score, for example, percentage of load loss in electricalsystem in response to the power quality event. In some embodiments, thepercentages of load loss may be used to calculate the severity.

At block 5715, a recovery time is determined for the system to recover(e.g., get back to normal, or close to normal) from each new powerquality event, for example, using techniques discussed in connectionwith figures above (e.g., FIG. 53).

At block 5720, information tags for each new quality event are added. Insome embodiments, these information tags may correspond to system addedor inferred information tags, as discussed above.

After block 5720, the method may end in some embodiments. In otherembodiments, the method may return to block 5705 and repeat again (e.g.,for additional tagging). In further embodiments, the method may includeone or more additional steps, as will be appreciated by one of ordinaryskill in the art.

Referring to FIG. 58, a flowchart illustrates an example method 5800 forgenerating dynamic tolerance curves, for example, after a learningperiod, such as the learning period discussed above in connection withFIG. 55. In accordance with some aspects, each metering location'sdynamic tolerance curve may be modeled after the learning, as describedabove in connection with FIGS. 34-40, and as will be appreciated fromfurther discussions below. Similar to method 5500, method 5800 may beimplemented on a processor of at least one metering device (e.g., 121,shown in FIG. 1A) and/or remote from the at least one metering device inat least one of: a cloud-based system, on-site software, a gateway, oranother head-end system.

As illustrated in FIG. 58, the method 5800 begins at block 5805, wherephases for each new power quality event in an electrical system arecalculated. For example, the phases for each new event may be reconciledby calculating the aggregated phases for each new power quality event(mean, max, min, etc., resulting in “all phases mean,” “worst phase,”“non-impacted phase,” etc.). In accordance with some aspects of thisdisclosure, the phases refer to the phases of a three-phase system here.A power quality event may originate on any one, two or all three phases;however, it can move to (involve) any other phases as the power qualityevent evolves. Aggregating the phases include evaluating the powerquality event from any one or two phases with respect to all threephases in the system. This approach can also be employed forsingle-phase systems with multiple legs (e.g., a typical house). Inaccordance with some aspects of this disclosure, the electrical systemmay be viewed through the lens of the three phases providing energy tosaid electrical system. Aggregations (and dynamic tolerance curves) mayoccur on each phase, independent of the other two phases. So, each phasein a three-phase system may be shown in its own dynamic tolerance curve,whether for a single metering location or from a system perspective. Forexample, producing a dynamic tolerance curve for Phase ‘A’ at a specificmetering location or producing a dynamic tolerance curve for Phase ‘A″that incorporates more than one metering device in the electricalsystem. These “devices” may also include virtual meters as well.

At block 5810, initial impact dynamic tolerance curves are generated orderived for each metering location in the electrical system.Additionally, at block 5815 final impact dynamic tolerance curves aregenerated or derived for each metering location in the electricalsystem. In some embodiments, the initial impact dynamic tolerance curvescorrespond to dynamic tolerance curves generated or derived fromelectrical measurement data captured by a metering device at eachmetering location at a first, initial time. Additionally, in someembodiments the final impact dynamic tolerance curves correspond todynamic tolerance curves generated or derived from electricalmeasurement data captured by a metering device at each metering locationat a second time after the first time. Both the initial impact dynamictolerance curves and the final impact dynamic tolerance curves mayindicate an impact of power quality events at the metering points in theelectrical system.

At block 5820, the function of sensitivity of the dynamic tolerancecurves may be calculated or approximated. In some embodiments, thefunction of sensitivity may be calculated or approximated based on theprogression of load loss as voltage drops progressively, between initialimpact and final impact.

At block 5825, acceptability thresholds for each metering point orlocation may be defined or inferred from tags (e.g., system or usertags) in the dynamic tolerance curves. For example, “no impactacceptable” may equate to “initial impact dynamic tolerance curve,” butmaybe 30% of load losses are deemed acceptable for a specific meter,while other meters may have a threshold at 10% acceptability. Inaccordance with some aspects of this disclosure, in the vast majority ofcases the loss of a load is either “acceptable” or “not acceptable.”When a meter is monitoring multiple loads that encompass both“acceptable” and “not acceptable” conditions, for example, thiscombination of “acceptable” and “not acceptable” impacts may be whatleads to unique thresholds at discrete metering locations.

At block 5830, which is optional in some embodiments, other systems(such as for example BMS, power SCADA, manufacturing SCADA) may add newcriticality scores (especially if no user or system tagging). In thecase of a manufacturing site with an industrial SCADA system, forexample, a criticality score may be calculated based on the load loss,e.g., with no additional user inputs. In some embodiments, thecriticality score may be inferred for each meter from the load loss. Forexample, “no load loss” may correspond to a criticality score of 0, “20%initial load loss” may correspond to a criticality score of 20, and “30%initial load loss” may correspond to a criticality score of 30, and soforth. In some embodiments, the industrial SCADA system may be linkedwith a Power SCADA, and some industrial processes may be mapped withsome of loads and meters in the electrical system. When measuringvoltage sags, for example, meters responsible for stopping criticalprocesses in the electrical system may be identified. This informationmay be added and fed into the Power SCADA, and the criticality scoresfor these meters may be updated (e.g., from 0 to 100).

At block 5835, criticality modeled dynamic tolerance curves aregenerated. For example, criticality tags may be modeled by the systemusing expert rules or feeding them into supervised learning algorithmsor other artificial intelligence (AI) tools to build a criticality modelfor each meter or metering point. In some embodiments, the criticalitymodel may reconcile potential discrepancies event by event and acrossall events of the meter, of user tags and other system tags. The outputis thus a criticality modeled dynamic tolerance curve (or curves). Inaccordance with some embodiments, the criticality modeled dynamictolerance curve(s) may be generated using simple rules, such as above,or other more complex calculations, for example, in response to one ormore user inputs. For example, the impact of a voltage sag on someproduction quality metrics may be monitored to determine how voltagesags impact the process (e.g., increase from 2 to 100 defective partsper million (ppm)). In parallel a user may tag the IEDs. IEDs that arelinked to the production may correct the original user criticalityscores by new “ppm-related scores.” In some embodiments, “ppm” may beapplied instead of “load loss” as a dynamic tolerance curve criticalitycalculation.

After block 5835, the method may end in some embodiments. In otherembodiments, the method may include one or more additional steps, aswill be appreciated by one of ordinary skill in the art.

Referring to FIG. 59, a flowchart illustrates an example method 5900 forleveraging and combining dynamic tolerance curves. Similar to method5800, method 5900 may be implemented on a processor of at least onemetering device (e.g., 121, shown in FIG. 1A) and/or remote from the atleast one metering device in at least one of: a cloud-based system,on-site software, a gateway, or another head-end system.

As illustrated in FIG. 59, the method 5900 begins at block 5905, wheremeter (or metering device) rankings and groups are determined ordefined. As discussed above, each metering device (e.g., IED, virtual,etc.) in an electrical system may be associated with a respectivemetering location (physical or virtual) in the electrical system. Insome embodiments, the meter rankings and groups are defined using thedifferent meter criticalities defined for each meter based on metercriticality scores (when available). Additionally, in some embodimentsmeter rankings and groups may be defined leveraging the relativeacceptability levels of different event tags (or non-acceptability).Manual configuration is also possible.

Example rankings according to the disclosure include, but are notlimited to:

-   -   “Initial impact” ranking based on magnitude values, e.g., start        with most sensitive to initial impact (e.g., the number of loads        dropped) and rank metering devices starting at 80% of nominal        voltage until, in one example, the last metering device, where        the initial impact only starts at 45% of nominal voltage.    -   “Final impact” ranking similar to the “initial impact” ranking,        except the magnitude of the final impact is used to rank the        metering devices.    -   For a “significant load loss” predetermined values (e.g., 25% of        load loss), the magnitude of the load loss may be used to rank        the metering devices.    -   “Criticality score” related ranking may be another possible        ranking.    -   “Size of loads” related ranking, e.g., using the maximum energy        consumption and ranking in descending order.

Example groupings according to the disclosure include, but are notlimited to:

-   -   Per load type (e.g., HVAC, motors, lighting, etc.).    -   Per physical or geographic location (per campus sector, per        building, per floor, per zone, etc.).    -   Per electric hierarchical branch or level (e.g., main meters,        branch meters, sub-meters, etc.).    -   Per sensitivity (e.g., based on the “initial impact” ranking,        infer coherent groups using clustering techniques in one        possible implementation).

At block 5910, dynamic tolerance curves associated with each meteringdevice (and metering point) are aggregated, for example, based on therankings and groups defined at block 5905. For example, at block 5905,each metering device may be grouped based three levels of criticality,e.g., high, medium, and low, as defined below.

-   -   High: IEDs that monitor process steps where voltage sags may        disrupt key process steps, generating long interruptions of        process and expensive waste of raw or semi-transformed material.    -   Medium: IEDs that monitor process steps where the voltage sags        result in a cost of non-quality production, but no major        disruption, even during interruptions.    -   Low: IEDs that monitor process steps where interruptions may be        ridden through without any identified or measurable impact.

A system user may want to have two (or more) different dynamic tolerancecurves to identify where SagFighers® or other mitigative solutionsshould be considered, and where any changes in sensitivity to voltagesags needs to be or should be monitored and evaluated. Assuming thethree levels of criticality are defined as set forth above, firstdynamic tolerance curves may be generated for each IED within the groupof “high” meters, e.g., using the “initial impact” to plot the curves ofeach meter. From there, an aggregate dynamic tolerance curve (i.e., afirst aggregate dynamic tolerance curve) may be generated with (orcontaining) all the values of initial impacts (taking into account themagnitude and duration). Additionally, a second aggregated dynamictolerance curve tracing the “voltage sag evolution” of each meter over agiven relevant time period (e.g., monthly or weekly) of the IED'svoltage sags may be generated. As one example, voltage sags may beindicated or plotted in a first color (e.g., red) in the tolerance curveif an IED is identified as becoming more sensitive (=new initial impactthreshold for that IED), and in a second color (e.g., light grey) in thetolerance curve if all the voltage sags of a meter fall within a knownor predetermined sensitivity.

In some embodiments, aggregate dynamic tolerance curve models may begenerated and graphs (or curves) may be generated in response to themodels, for example, for the different defined sub-groups of meters orall meters to illustrate the different thresholds and zones in thedynamic tolerance curves.

At block 5915, the aggregated dynamic tolerance curves may be analyzed,for example, for different sub-groups of meters or all meters. Forexample, for each group normal dynamic tolerance curves may be defined,and abnormalities may be highlighted (such as abnormally high or lowsensitivities meters/curves). For a group of IEDs monitoring differentload types, for example, a user may want to see which IEDs monitoringmotors (i.e., one example type of loads) are more sensitive to voltagesags than others. The user may thus ask for an analysis to provide alist of the IEDs that are significantly more sensitive to voltage sags(from the other IEDs). This requires an analysis which can be based onseveral steps. For example, cluster groups of meters or identifyoutliers or extreme outliers based on median values+interquartile range(IQR)*1.5 or IQR*3. This then can be done for “initial impact”, “finalimpact” and/or for any given value (such as 25% of loads lost).

Additionally, link analysis, time analysis, location analysis, flowanalysis, spread analysis, etc. may be conducted to identify possibledrivers and pre-occurrences or co-occurrences (switching of loads or BMSor SCADA control actions), recurring patterns (day of week, daily hourlypatterns), possible sources (location in hierarchy), etc.

After block 5915, the method may end in some embodiments. In otherembodiments, the method may include one or more additional steps, aswill be appreciated by one of ordinary skill in the art.

As illustrated above, and as will be appreciated by one of ordinaryskill in the art, embodiments of the disclosure promote “more andbetter” metering within facilities. For example, the more IEDs installedin an energy consumer's electrical system, the more beneficial theseembodiments may be for the energy consumer. As will also be appreciatedby one of ordinary skill in the art, there are significant opportunitiesfor voltage event mitigation products. Further, it will be appreciatedby one of ordinary skill in the art that it is important to identify andpromote opportunities that would have typically been overlooked,misunderstood, or simply ignored by energy consumers. The ability toquantify voltage events creates a justifiable sense of urgency for theenergy consumer to resolve these issues. The various embodimentsdescribed in this disclosure should allow services-based organizationsto more readily identify opportunities and be retained for designing andinstalling the most economical solution. By leveraging products toidentify opportunities for improving voltage event mitigation andreduced recovery time, for example, energy consumers may improve theiroperational availability and increase their profitability.

The embodiments described in this disclosure may also create manyopportunities for cloud-based services. While the prospect of usingon-site software to evaluate, quantify, and mitigate voltage events maybe more ideal in some embodiments, direct (or substantially direct)participation/interaction with energy consumers may tend to promote manymore services and products sales opportunities. By evaluating thevoltage event data in the cloud, active engagement in a timelier mannerwith relevant information and practical solutions may yield furtherpossibilities.

As illustrated above, voltage sags/dips have a significant impact onindustrial equipment, processes, products, and ultimately a customer'sbottom-line. In embodiments, voltage sags/dips are the biggest (or closeto the biggest) source of power quality issues, and can originate bothinside and outside an energy consumer's facility. Using dynamic voltagetolerance curves and the other embodiments described herein will providethe ability to localize, quantify, and rectify the impact of voltagesags/dips and shorten event recovery time. Moreover, dynamic voltagetolerance curves provide the ability to target, design and validatecustom mitigative solutions and services, which helps the energyconsumer reduce interruptions to their operations, maximize their systemperformance and availability, increase their equipment life, and reducetheir total operating costs. In short, the embodiments disclosed in thisapplication may be incorporated in meters, gateways, on-site softwaresuch as PME, and cloud-based offers such as Power Advisor by SchneiderElectric.

As described above and as will be appreciated by those of ordinary skillin the art, embodiments of the disclosure herein may be configured as asystem, method, or combination thereof. Accordingly, embodiments of thepresent disclosure may be comprised of various means including hardware,software, firmware or any combination thereof.

It is to be appreciated that the concepts, systems, circuits andtechniques sought to be protected herein are not limited to use in theexample applications described herein (e.g., power monitoring systemapplications) but rather, may be useful in substantially any applicationwhere it is desired to manage power quality events in an electricalsystem. While particular embodiments and applications of the presentdisclosure have been illustrated and described, it is to be understoodthat embodiments of the disclosure not limited to the preciseconstruction and compositions disclosed herein and that variousmodifications, changes, and variations can be apparent from theforegoing descriptions without departing from the spirit and scope ofthe disclosure as defined in the appended claims.

Having described preferred embodiments, which serve to illustratevarious concepts, structures and techniques that are the subject of thispatent, it will now become apparent to those of ordinary skill in theart that other embodiments incorporating these concepts, structures andtechniques may be used. Additionally, elements of different embodimentsdescribed herein may be combined to form other embodiments notspecifically set forth above.

Accordingly, it is submitted that that scope of the patent should not belimited to the described embodiments but rather should be limited onlyby the spirit and scope of the following claims.

What is claimed is:
 1. A method for analyzing power quality events in anelectrical system, comprising: processing electrical measurement datafrom or derived from energy-related signals captured by a plurality ofmetering devices to generate or update a plurality of dynamic tolerancecurves, wherein each of the plurality of dynamic tolerance curvescharacterizes a response characteristic of the electrical system at arespective metering point of a plurality of metering points in theelectrical system; selectively aggregating power quality data from theplurality of dynamic tolerance curves; analyzing power quality events inthe electrical system based on the selectively aggregated power qualitydata; and adjusting or controlling one or more parameters, processes,conditions or loads associated with the electrical system in response tothe power quality events analyzed.
 2. The method of claim 1, wherein theplurality of dynamic tolerance curves are generated or updated bydeducing relevant signal characteristics from the electrical measurementdata.
 3. The method of claim 2, wherein the relevant signalcharacteristics include at least one of: magnitude, duration, powerquality type, time of occurrence, process(es) involved, location,devices impacted, relative or absolute impact, recovery time, andperiodicity of events or event types.
 4. The method of claim 2, whereinthe relevant signal characteristics include at least one of: identifiedchanges in current and/or phase shifts which result in an interpretationor interpretations.
 5. The method of claim 4, wherein the interpretationor interpretations include inductive or capacitive load type added orremoved from the electrical system and/or percentage of pre-event loadsadded or dropped from the electrical system during an event.
 6. Themethod of claim 2, wherein the relevant signal characteristics aredetermined from portions of the electrical measurement data from priorto a start time of the power quality events, and from portions of theelectrical measurement data from after an end time of the power qualityevents.
 7. The method of claim 2, wherein the relevant signalcharacteristics are determined from portions of the electricalmeasurement data from during the power quality events.
 8. The method ofclaim 1, wherein the plurality of dynamic tolerance curves are generatedor updated after a learning period.
 9. The method of claim 8, whereinthresholds for triggering alarms are generated during or after thelearning period during generation or updates of the dynamic tolerancecurves.
 10. The method of claim 9, wherein the learning period continuesuntil there is sufficient data to generate the thresholds.
 11. Themethod of claim 1, wherein processing electrical measurement data fromor derived from energy-related signals captured by a plurality ofmetering devices in the electrical system to generate or update aplurality of dynamic tolerance curves, comprises: processing electricalmeasurement data from or derived from energy-related signals captured bythe plurality of metering devices at a first, initial time to generateor derive initial impact dynamic tolerance curves for each meteringpoint in the electrical system; and processing electrical measurementdata from or derived from energy-related signals captured by theplurality of metering devices at a second time after the first, initialtime to generate or derive final impact dynamic tolerance curves foreach metering location in the electrical system, wherein the pluralityof dynamic tolerance curves include the initial impact dynamic tolerancecurves and the final impact dynamic tolerance curves, and the initialimpact dynamic tolerance curves and the final impact dynamic tolerancecurves indicate an impact of the power quality events at the meteringpoints in the electrical system.
 12. The method of claim 11, furthercomprising: calculating or approximating the function of sensitivity ofthe plurality of dynamic tolerance curves based on the progression ofload loss as voltage drops progressively, between initial impact andfinal impact, as indicated in the initial impact dynamic tolerancecurves and the final impact dynamic tolerance curves.
 13. The method ofclaim 12, further comprising: defining or inferring acceptabilitythresholds for each metering point from tags in the plurality of dynamictolerance curves, the acceptability thresholds indicating acceptable andnot acceptable impacts or conditions for each metering point.
 14. Themethod of claim 12, further comprising: calculating criticality scoresbased on the load loss and tagging the criticality scores withassociated criticality tags; and generating criticality modeled dynamictolerance curves in response to using expert rules or feeding thecriticality tags into supervised learning algorithms or other artificialintelligence (AI) tools to build a criticality model for each of theplurality of metering device or the plurality of metering points. 15.The method of claim 1, wherein the one or more parameters, processes,conditions or loads are dynamically adjusted or controlled by a controlsystem associated with the electrical system.
 16. The method of claim 1,wherein the loads include a plurality of loads monitored by theplurality of metering devices.
 17. The method of claim 16, furthercomprising: evaluating initial impact thresholds and/or final impactthresholds, and positions in between the initial impact thresholdsand/or the final impact thresholds, to determine percentage of theplurality of loads lost in response to each of the power quality events,wherein the initial impact thresholds correspond to thresholds fordetecting a first percentage of the plurality of loads lost, and thefinal impact thresholds correspond to thresholds for detecting when allof the plurality of loads are lost.
 18. The method of claim 17, whereinthe positions between the initial impact thresholds and/or the finalimpact thresholds are determined using one or more models.
 19. Themethod of claim 18, wherein the one or more models include at least oneof a linear model, a logarithmic model, and a curve model.
 20. Themethod of claim 19, wherein the curve model is determined bysubstantially any approximated or modeled line or curve calculation orcurve fitting algorithm
 21. The method of claim 18, wherein the one ormore models are used to approximate how the electrical system respondsto each of the power quality events.
 22. The method of claim 21, whereinthe power quality events include a voltage event, and the one or moremodels are used to interpolate the voltage event's impact on theelectrical system.
 23. A system for analyzing power quality events in anelectrical system, comprising: at least one input coupled to at least aplurality of metering devices in the electrical system; at least oneoutput coupled to at least a plurality of loads monitored by theplurality of metering devices; and a processor coupled to receiveelectrical measurement data from or derived from energy-related signalscaptured by the plurality of metering devices from the at least onesystem input, the processor configured to: process the electricalmeasurement data to generate or update a plurality of dynamic tolerancecurves, wherein each of the plurality of dynamic tolerance curvescharacterizes a response characteristic of the electrical system at arespective metering point of a plurality of metering points in theelectrical system; selectively aggregate power quality data from theplurality of dynamic tolerance curves; analyze power quality events inthe electrical system based on the selectively aggregated power qualitydata; and adjust or control one or more parameters, processes,conditions or loads associated with the electrical system in response tothe power quality events analyzed.
 24. The system of claim 23, whereinthe plurality of dynamic tolerance curves are generated or updated bydeducing relevant signal characteristics from the electrical measurementdata.
 25. The system of claim 24, wherein the relevant signalcharacteristics include at least one of: magnitude, duration, powerquality type, time of occurrence, process(es) involved, location,devices impacted, relative or absolute impact, recovery time, andperiodicity of events or event types.
 26. The system of claim 24,wherein the relevant signal characteristics include at least one of:identified changes in current and/or phase shifts which result in aninterpretation or interpretations.
 27. The system of claim 26, whereinthe interpretation or interpretations include inductive or capacitiveload type added or removed from the electrical system and/or percentageof pre-event loads added or dropped from the electrical system during anevent.
 28. The system of claim 24, wherein the relevant signalcharacteristics are determined from portions of the electricalmeasurement data from prior to a start time of the power quality events,and from portions of the electrical measurement data from after an endtime of the power quality events.
 29. The system of claim 24, whereinthe relevant signal characteristics are determined from portions of theelectrical measurement data from during the power quality events. 30.The system of claim 23, wherein the plurality of dynamic tolerancecurves are generated or updated after a learning period.
 31. The systemof claim 30, wherein thresholds for triggering alarms are generatedduring or after the learning period during generation or updates of thedynamic tolerance curves.
 32. The system of claim 31, wherein thelearning period continues until there is sufficient data to generate thethresholds.
 33. The system of claim 23, wherein processing theelectrical measurement data to generate or update a plurality of dynamictolerance curves, comprises: processing electrical measurement data fromor derived from energy-related signals captured by the plurality ofmetering devices at a first, initial time to generate or derive initialimpact dynamic tolerance curves for each metering point in theelectrical system; and processing electrical measurement data from orderived from energy-related signals captured by the plurality ofmetering devices at a second time after the first, initial time togenerate or derive final impact dynamic tolerance curves for eachmetering location in the electrical system, wherein the plurality ofdynamic tolerance curves include the initial impact dynamic tolerancecurves and the final impact dynamic tolerance curves, and the initialimpact dynamic tolerance curves and the final impact dynamic tolerancecurves indicate an impact of the power quality events at the meteringpoints in the electrical system.
 34. The system of claim 33, wherein theprocessor is further configured to: calculate or approximate thefunction of sensitivity of the plurality of dynamic tolerance curvesbased on the progression of load loss as voltage drops progressively,between initial impact and final impact, as indicated in the initialimpact dynamic tolerance curves and the final impact dynamic tolerancecurves.
 35. The system of claim 34, wherein the processor is furtherconfigured to: define or infer acceptability thresholds for eachmetering point from tags in the plurality of dynamic tolerance curves,the acceptability thresholds indicating acceptable and not acceptableimpacts or conditions for each metering point.
 36. The system of claim34, wherein the processor is further configured to: calculatecriticality scores based on the load loss and tagging the criticalityscores with associated criticality tags; and generate criticalitymodeled dynamic tolerance curves in response to using expert rules orfeeding the criticality tags into supervised learning algorithms orother artificial intelligence (AI) tools to build a criticality modelfor each of the plurality of metering device or the plurality ofmetering points.
 37. The system of claim 23, wherein the one or moreparameters, processes, conditions or loads are dynamically adjusted orcontrolled by a control system associated with the electrical system.38. The system of claim 23, wherein the loads include a plurality ofloads monitored by the plurality of metering devices.
 39. The system ofclaim 38, wherein the processor is further configured to: evaluateinitial impact thresholds and/or final impact thresholds, and positionsin between the initial impact thresholds and/or the final impactthresholds, to determine percentage of the plurality of loads lost inresponse to each of the power quality events, wherein the initial impactthresholds correspond to thresholds for detecting a first percentage ofthe plurality of loads lost, and the final impact thresholds correspondto thresholds for detecting when all of the plurality of loads are lost.40. The system of claim 39, wherein the positions between the initialimpact thresholds and/or the final impact thresholds are determinedusing one or more models.
 41. The system of claim 40, wherein the one ormore models include at least one of a linear model, a logarithmic model,and a curve model.
 42. The system of claim 41, wherein the curve modelis determined by substantially any approximated or modeled line or curvecalculation or curve fitting algorithm
 43. The system of claim 40,wherein the one or more models are used to approximate how theelectrical system responds to each of the power quality events.
 44. Thesystem of claim 43, wherein the power quality events include a voltageevent, and the one or more models are used to interpolate the voltageevent's impact on the electrical system.